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a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.no.png and /dev/null differ diff --git a/translations/fi/README.md b/translations/fi/README.md index 1f300c3c..6cce3e52 100644 --- a/translations/fi/README.md +++ b/translations/fi/README.md @@ -1,8 +1,8 @@ -[arabia](../ar/README.md) | [bengali](../bn/README.md) | [bulgaria](../bg/README.md) | [burma (Myanmar)](../my/README.md) | [kiina (yksinkertaistettu)](../zh/README.md) | [kiina (perinteinen, Hong Kong)](../hk/README.md) | [kiina (perinteinen, Macao)](../mo/README.md) | [kiina (perinteinen, Taiwan)](../tw/README.md) | [kroatia](../hr/README.md) | [tšekki](../cs/README.md) | [tanska](../da/README.md) | [hollanti](../nl/README.md) | [viro](../et/README.md) | [suomi](./README.md) | [ranska](../fr/README.md) | [saksa](../de/README.md) | [kreikka](../el/README.md) | [heprea](../he/README.md) | [hindi](../hi/README.md) | [unkari](../hu/README.md) | [indonesia](../id/README.md) | [italia](../it/README.md) | [japani](../ja/README.md) | [kannada](../kn/README.md) | [korea](../ko/README.md) | [liettua](../lt/README.md) | [malaiji](../ms/README.md) | [malayalam](../ml/README.md) | [marathi](../mr/README.md) | [nepali](../ne/README.md) | [nigerian pidgin](../pcm/README.md) | [norja](../no/README.md) | [persia (farsi)](../fa/README.md) | [puola](../pl/README.md) | [portugali (Brasil)](../br/README.md) | [portugali (Portugali)](../pt/README.md) | [pandžabi (Gurmukhi)](../pa/README.md) | [romania](../ro/README.md) | [venäjä](../ru/README.md) | [serbia (kyrillinen)](../sr/README.md) | [slovakki](../sk/README.md) | [sloveeni](../sl/README.md) | [espanja](../es/README.md) | [swahili](../sw/README.md) | [ruotsi](../sv/README.md) | [tagalog (filipino)](../tl/README.md) | [tamili](../ta/README.md) | [telugu](../te/README.md) | [thai](../th/README.md) | [turkki](../tr/README.md) | [ukraina](../uk/README.md) | [urdu](../ur/README.md) | [vietnam](../vi/README.md) +[Arabia](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgaria](../bg/README.md) | [Burma (Myanmar)](../my/README.md) | [Kiina (yksinkertaistettu)](../zh/README.md) | [Kiina (perinteinen, Hong Kong)](../hk/README.md) | [Kiina (perinteinen, Macao)](../mo/README.md) | [Kiina (perinteinen, Taiwan)](../tw/README.md) | [Kroatia](../hr/README.md) | [Tšekki](../cs/README.md) | [Tanska](../da/README.md) | [Hollanti](../nl/README.md) | [Viro](../et/README.md) | [Suomi](./README.md) | [Ranska](../fr/README.md) | [Saksa](../de/README.md) | [Kreikka](../el/README.md) | [Heprea](../he/README.md) | [Hindi](../hi/README.md) | [Unkari](../hu/README.md) | [Indonesia](../id/README.md) | [Italia](../it/README.md) | [Japani](../ja/README.md) | [Kannada](../kn/README.md) | [Korea](../ko/README.md) | [Liettua](../lt/README.md) | [Malaiji](../ms/README.md) | [Malajalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norja](../no/README.md) | [Persia (Farsi)](../fa/README.md) | [Puola](../pl/README.md) | [Portugali (Brasilia)](../br/README.md) | [Portugali (Portugali)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romania](../ro/README.md) | [Venäjä](../ru/README.md) | [Serbia (kyrillinen)](../sr/README.md) | [Slovakki](../sk/README.md) | [Sloveeni](../sl/README.md) | [Espanja](../es/README.md) | [Swahili](../sw/README.md) | [Ruotsi](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamili](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkki](../tr/README.md) | [Ukraina](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnam](../vi/README.md) > **Haluatko mieluummin kloonata paikallisesti?** -> Tämä arkisto sisältää yli 50 käännöskieltä, mikä kasvattaa merkittävästi latauskokoa. Lataaksesi ilman käännöksiä, käytä sparse checkout -menetelmää: +> Tässä arkistossa on yli 50 kielen käännökset, jotka kasvattavat merkittävästi latauskokoa. Jos haluat kloonata ilman käännöksiä, käytä sparse checkoutia: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Tämä antaa sinulle kaiken tarvittavan kurssin suorittamiseen paljon nopeammalla latauksella. +> Tämä antaa sinulle kaiken, mitä tarvitset kurssin suorittamiseen paljon nopeammalla latauksella. -**Jos haluat lisäkieliä käännöksiksi, tuetut kielet löytyvät [tästä](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Jos haluat lisäkieliä käännöksiksi, tuetut kielet löytyvät [täältä](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Liity yhteisöön [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Mitä opit -**[Kurssin mielenkartta](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Kurssin miellekartta](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** Tässä opetussuunnitelmassa opit: -* Eri lähestymistapoja tekoälyyn, mukaan lukien "hyvä vanha" symbolinen lähestymistapa, jossa käsitellään **tiedon esitystä** ja päättelyä ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Neuroverkot** ja **syväoppiminen**, jotka ovat modernin tekoälyn ytimessä. Havainnollistamme näitä tärkeitä aiheita koodin avulla kahdessa suositussa kehyksessä - [TensorFlow](http://Tensorflow.org) ja [PyTorch](http://pytorch.org). -* **Neuroarkkitehtuurit** kuvien ja tekstin käsittelyyn. Käymme läpi viimeisimpiä malleja, vaikkakin osittain hieman jäljessä alan huipputasosta. -* Vähemmän tunnettuja tekoälyn lähestymistapoja, kuten **geneettisiä algoritmeja** ja **moniagenttijärjestelmiä**. +* Eri lähestymistapoja tekoälyyn, mukaan lukien "hyvä vanha" symbolinen lähestymistapa **Tietämyksen esittämisen** ja päättelyn kanssa ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Neuroverkkoja** ja **Syväoppimista**, jotka ovat modernin tekoälyn ytimessä. Havainnollistamme näiden tärkeiden aiheiden taustalla olevia käsitteitä käyttämällä kahta suosittua kehystä - [TensorFlow](http://Tensorflow.org) ja [PyTorch](http://pytorch.org). +* **Neuroarkkitehtuureja** kuvan ja tekstin käsittelyyn. Käymme läpi uusia malleja, mutta saatamme olla hieman jäljessä viimeisimmästä kehityksestä. +* Vähemmän suosittuja tekoälyn lähestymistapoja, kuten **Geneettisiä algoritmeja** ja **Moni-agenttijärjestelmiä**. -Mitään ei käsitellä tässä opetussuunnitelmassa: +Mitä emme käsittele tässä opetussuunnitelmassa: -> [Löydä kaikki kurssin lisäresurssit Microsoft Learn -kokoelmastamme](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Löydä kaikki tämän kurssin lisäresurssit Microsoft Learn -kokoelmastamme](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Liiketoimintatapauksia **tekoälyn käytöstä liiketoiminnassa**. Voit harkita [Tekoälyn perusteet liiketoimintakäyttäjille](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) -oppipolkua Microsoft Learnissa tai [AI Business Schoolia](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), joka on kehitetty yhteistyössä [INSEADin](https://www.insead.edu/) kanssa. -* **Perinteistä koneoppimista**, jota on hyvin kuvattu meidän [Koneoppimisen perusteet -opinnoissamme](http://github.com/Microsoft/ML-for-Beginners). -* Käytännön tekoälysovelluksia, jotka on rakennettu käyttäen **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** -palveluita. Tässä suosittelemme aloittamaan Microsoft Learnin visio-, luonnollisen kielen käsittely- ja **[Generatiivisen tekoälyn Azure OpenAI -palvelulla](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** -moduuleilla. -* Erityisiä ML **pilvikehyksiä**, kuten [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) tai [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Harkitse käyttäväsi [Rakenna ja operoi koneoppimisratkaisuja Azure Machine Learningillä](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ja [Rakenna ja operoi koneoppimisratkaisuja Azure Databricksillä](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) oppipolkuja. -* **Keskustelupohjainen tekoäly** ja **chatbotit**. On olemassa erillinen [Luo keskustelupohjaisia tekoälyratkaisuja](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) -oppipolku ja voit myös katsoa [tämän blogikirjoituksen](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) lisätietoja varten. -* **Syvempi matematiikka** syväoppimisen takana. Tässä suosittelemme [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) -kirjaa, jonka ovat kirjoittaneet Ian Goodfellow, Yoshua Bengio ja Aaron Courville ja joka on myös saatavilla verkossa osoitteessa [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Liiketoiminnan käyttötapauksia, joissa hyödynnetään **tekoälyä liiketoiminnassa**. Suosittelemme tutustumaan Microsoft Learnin [Johdatus tekoälyyn liiketoiminnan käyttäjille](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) oppimispolkuun tai [AI Business Schooliin](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), joka on kehitetty yhteistyössä [INSEADin](https://www.insead.edu/) kanssa. +* **Perinteistä koneoppimista**, joka on hyvin kuvattu opetussuunnitelmassamme [Machine Learning for Beginners](http://github.com/Microsoft/ML-for-Beginners). +* Käytännön tekoälysovelluksia, jotka on rakennettu käyttäen **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** -palveluita. Tätä varten suosittelemme aloittamaan Microsoft Learnin moduuleista näköaistilla ([vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)), luonnollisen kielen prosessoinnilla ([natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)), **[Generatiivisella tekoälyllä Azure OpenAI -palvelulla](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ja muilla. +* Tiettyjä ML **pilvikehyksiä**, kuten [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), tai [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Otamme huomioon [Rakenna ja hallinnoi koneoppimisratkaisuja Azure Machine Learningilla](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ja [Rakenna ja hallinnoi koneoppimisratkaisuja Azure Databricksilla](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) oppimispolut. +* **Keskustelullista tekoälyä** ja **chatbotteja**. Näihin on oma [Luo keskustelullisia tekoälyratkaisuja](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) oppimispolkunsa, ja tarkempaa tietoa löydät myös [tästä blogikirjoituksesta](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/). +* **Syvä matematiikka** syväoppimisen taustalla. Tätä varten suosittelemme Ian Goodfellow'n, Yoshua Bengion ja Aaron Courvillen teosta [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618), joka on myös saatavilla verkossa osoitteessa [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Kepeään johdantoon _tekoäly pilvessä_ -aiheisiin kannattaa tutustua [Aloita tekoäly Azurella](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) -oppipolulla. +Matala-asteinen johdanto _tekoälyyn pilvessä_ -aiheisiin löytyy Microsoft Leanin [Aloita tekoälyn kanssa Azurella](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) - oppimispolulta. # Sisältö -| | Oppitunnin linkki | PyTorch/Keras/TensorFlow | Laboratorio | +| | Oppitunnin linkki | PyTorch/Keras/TensorFlow | Lab | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Kurssin asetukset](./lessons/0-course-setup/setup.md) | [Kokoa kehitysympäristösi](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Johdanto tekoälyyn**](./lessons/1-Intro/README.md) | | | +| 0 | [Kurssin asetukset](./lessons/0-course-setup/setup.md) | [Ota kehitysympäristö käyttöön](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Johdatus tekoälyyn**](./lessons/1-Intro/README.md) | | | | 01 | [Johdanto ja tekoälyn historia](./lessons/1-Intro/README.md) | - | - | -| II | **Symbolinen tekoäly** | -| 02 | [Tiedon esitys ja asiantuntijajärjestelmät](./lessons/2-Symbolic/README.md) | [Asiantuntijajärjestelmät](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Käsitemalli](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**Johdanto neuroverkkoihin**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptroni](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Muistikirja](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Harjoitus](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Monikerroksinen perceptroni ja oman kehysympäristön luominen](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Muistikirja](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Harjoitus](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Johdanto kehyksiin (PyTorch/TensorFlow) ja ylisovittaminen](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Harjoitus](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Koneellinen näkö**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Tutustu koneelliseen näkökenttään Microsoft Azurella](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Johdanto koneelliseen näkökenttään. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Muistikirja](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Harjoitus](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Konvoluutiohermoverkot](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN-arkkitehtuurit](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Harjoitus](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Esikoulutetut verkot ja siirto-oppiminen](./lessons/4-ComputerVision/08-TransferLearning/README.md) ja [Koulutusvinkkejä](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Harjoitus](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autokooderit ja VAE:t](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generatiiviset vastustukselliset verkot & taiteellinen tyylinsiirto](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Kohteiden tunnistus](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Harjoitus](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| II | **Sykolinen tekoäly** | +| 02 | [Tietämyksen esittäminen ja asiantuntijajärjestelmät](./lessons/2-Symbolic/README.md) | [Asiantuntijajärjestelmät](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Käsiteverkko](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**Johdatus neuroverkkoihin**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Monikerroksinen Perceptron ja Oman Kehyksen Luominen](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Johdanto kehyksiin (PyTorch/TensorFlow) ja Ylilyönti](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Tietokonenäkö**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Tutustu tietokonenäköön Microsoft Azurella](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Johdanto tietokonenäköön. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Konvoluutioneuroverkot](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN-arkkitehtuurit](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Esikoulutetut verkot ja siirto-oppiminen](./lessons/4-ComputerVision/08-TransferLearning/README.md) ja [Koulutusvinkit](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoenkooderit ja VAE:t](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Generatiiviset vastakkainasettelumallit ja taiteellinen siirto](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Kohteen tunnistus](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semanttinen segmentointi. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**Luonnollisen kielen käsittely**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Tutustu luonnollisen kielen käsittelyyn Microsoft Azurella](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Tekstin esitys. Kukka-/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Semanttiset sanan mobiilit. Word2Vec ja GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Kielimallinnus. Oman upotuksen kouluttaminen](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Harjoitus](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Takautuvat hermoverkot](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Generatiiviset takautuvat verkot](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Harjoitus](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 13 | [Tekstin esitys. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [Semanttiset sanasijoitukset. Word2Vec ja GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Kielimallinnus. Oman upotuksen kouluttaminen](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Toistuvat neuroverkot](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [Generatiiviset toistuvat verkot](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Nimettyjen entiteettien tunnistus](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Harjoitus](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Suurten kielimallien, kehotusohjelmoinnin ja vähän tehtäviä](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [Nimettyjen entiteettien tunnistus](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Laajat kielimallit, kehotteiden ohjelmointi ja vähäiset oppimistehtävät](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Muut tekoälytekniikat** || | -| 21 | [Geneettiset algoritmit](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Muistikirja](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Syvä vahvistusoppiminen](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Harjoitus](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Moni-agenttijärjestelmät](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 21 | [Geneettiset algoritmit](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Syvävahvistusoppiminen](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Moniagenttijärjestelmät](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Tekoälyn etiikka** | | | | 24 | [Tekoälyn etiikka ja vastuullinen tekoäly](./lessons/7-Ethics/README.md) | [Microsoft Learn: Vastuullisen tekoälyn periaatteet](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Lisää** | | | -| 25 | [Monimodaaliset verkot, CLIP ja VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Muistikirja](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Monimodaaliset verkot, CLIP ja VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Jokainen oppitunti sisältää -* Esilukemateriaalia -* Suoritettavia Jupyter-muistikirjoja, jotka ovat usein kehykselle (**PyTorch** tai **TensorFlow**) spesifisiä. Suoritettava muistikirja sisältää myös paljon teoreettista materiaalia, joten aiheen ymmärtämiseksi tarvitsee käydä läpi ainakin yksi muistikirjan versio (joko PyTorch tai TensorFlow). -* Joihinkin aiheisiin saatavilla olevia **harjoituksia**, jotka antavat sinulle mahdollisuuden kokeilla oppimasi materiaalin soveltamista konkreettiseen ongelmaan. -* Joissakin osissa linkkejä [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) moduuleihin, jotka käsittelevät aiheeseen liittyviä aiheita. +* Ennakkolukemista materiaalia +* Suoritettavia Jupyter-muistikirjoja, jotka ovat usein kehyspohjaisia (**PyTorch** tai **TensorFlow**). Suoritettava muistikirja sisältää myös paljon teoreettista materiaalia, joten aiheen ymmärtämiseksi sinun täytyy käydä läpi ainakin yksi versio muistikirjasta (PyTorch tai TensorFlow). +* **Labroja** tarjolla joillekin aiheille, jotka antavat sinulle mahdollisuuden kokeilla oppimaasi käytännössä tietyn ongelman ratkaisemiseksi. +* Joissakin osioissa on linkkejä [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -moduuleihin, jotka käsittelevät aiheeseen liittyviä aiheita. ## Aloittaminen ### 🎯 Uusi tekoälyssä? Aloita tästä! -Jos olet täysin uusi tekoälyssä ja haluat nopeita, käytännön esimerkkejä, tutustu [**aloittelijaystävällisiin esimerkkeihimme**](./examples/README.md)! Niihin kuuluvat: +Jos olet täysin uusi tekoälyssä ja haluat nopeita, käytännön esimerkkejä, tutustu [**Aloittelijaystävällisiin esimerkkeihin**](./examples/README.md)! Näihin sisältyy: -- 🌟 **Hei tekoäly maailma** - Ensimmäinen tekoälyohjelmasi (kuvion tunnistus) -- 🧠 **Yksinkertainen hermoverkko** - Rakenna hermoverkko alusta alkaen -- 🖼️ **Kuvien luokittelija** - Luokittele kuvia yksityiskohtaisilla kommenteilla -- 💬 **Tekstin tunnelma** - Analysoi positiivista/kai­tavaa tekstiä +- 🌟 **Hei tekoälymaailma** - Ensimmäinen tekoälyohjelmasi (kuvion tunnistus) +- 🧠 **Yksinkertainen neuroverkko** - Luo neuroverkko alusta alkaen +- 🖼️ **Kuvien luokittelija** - Kuvien luokittelu yksityiskohtaisilla kommenteilla +- 💬 **Tekstin tunnelma** - Analysoi positiivista/negatiivista tekstiä -Nämä esimerkit on suunniteltu auttamaan sinua ymmärtämään tekoälyn käsitteitä ennen kuin sukellat koko oppiaineistoon. +Nämä esimerkit on suunniteltu auttamaan sinua ymmärtämään tekoälyn käsitteitä ennen kuin sukellat koko opetussuunnitelmaan. -### 📚 Koko oppiaineiston asennus +### 📚 Koko opetussuunnitelman asennus -- Olemme luoneet [asennustunnin](./lessons/0-course-setup/setup.md) auttamaan sinua kehitysympäristön asennuksessa. - Opettajille olemme myös luoneet [oppiaineiston asennustunnin](./lessons/0-course-setup/for-teachers.md)! -- Kuinka [ajaa koodi VSCode- tai Codepace-ympäristössä](./lessons/0-course-setup/how-to-run.md) +- Olemme luoneet [asennustunnin](./lessons/0-course-setup/setup.md) auttamaan sinua kehitysympäristön perustamisessa. - Opettajille olemme myös luoneet [opetussuunnitelman asennustunnin](./lessons/0-course-setup/for-teachers.md)! +- Kuinka [ajaa koodi VSCodeissa tai Codespacessa](./lessons/0-course-setup/how-to-run.md) -Seuraa näitä vaiheita: +Noudata näitä ohjeita: -Tee varastosta haarukka: Klikkaa "Fork"-painiketta tämän sivun yläoikeassa kulmassa. +Forkkaa repositorio: Klikkaa tämän sivun oikeasta yläkulmasta "Fork" -painiketta. -Kloonaa varasto: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Kloonaa repositorio: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Älä unohda tähdätä (🌟) tätä varastoa, jotta löydät sen helpommin myöhemmin. +Älä unohda tähdätä (🌟) tätä repoosi helpottaaksesi sen löytymistä myöhemmin. -## Tapaa muut oppijat +## Tapaa muita oppijoita -Liity [viralliselle AI Discord -palvelimellemme](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) tavata ja verkostoitua muiden tämän kurssin kävijöiden kanssa sekä saada tukea. +Liity viralliseen [AI Discord -palvelimeemme](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) tavata ja verkostoitua kurssin muiden oppijoiden kanssa ja saadaksesi tukea. -Jos sinulla on palautetta tuotteesta tai kysymyksiä rakentamisen aikana, käy [Azure AI Foundry Developer Forumilla](https://aka.ms/foundry/forum) +Jos sinulla on palautetta tuotteesta tai kysymyksiä rakentamisen aikana, käy [Azure AI Foundry Developer Forumissa](https://aka.ms/foundry/forum) ## Kyselyt -> **Huomautus kyselyistä**: Kaikki kyselyt ovat Quiz-app-kansiossa polussa etc\quiz-app, tai [verkkoversio täällä](https://ff-quizzes.netlify.app/). Ne on linkitetty oppitunneilta, kyselysovellus voidaan ajaa paikallisesti tai ottaa käyttöön Azureen; seuraa ohjeita `quiz-app`-kansiossa. Ne ovat asteittain lokalisoituna. +> **Huomautus kyselyistä**: Kaikki kyselyt löytyvät Quiz-app-kansiosta polusta etc\quiz-app, tai [online tässä](https://ff-quizzes.netlify.app/) Ne ovat linkitettynä tunneissa, kyselysovellusta voi ajaa paikallisesti tai ottaa käyttöön Azure:ssa; noudata ohjeita `quiz-app`-kansiossa. Ne ovat asteittain lokalisoitumassa. -## Apu tarvitaan +## Apua kaivataan -Onko sinulla ehdotuksia tai oletko löytänyt kirjoitusvirheitä tai koodivirheitä? Luo issue tai tee vetopyyntö. +Onko sinulla ehdotuksia tai oletko löytänyt kirjoitus- tai koodivirheitä? Avaa issue tai tee pull request. ## Erityiskiitokset -* **✍️ Pääkirjoittaja:** [Dmitry Soshnikov](http://soshnikov.com), tohtori +* **✍️ Päätekijä:** [Dmitry Soshnikov](http://soshnikov.com), tohtori * **🔥 Toimittaja:** [Jen Looper](https://twitter.com/jenlooper), tohtori -* **🎨 Muistipiirroskuvittaja:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 Luonnostelija:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Kyselyjen tekijä:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Keskeiset kontribuuttorit:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 Ydinosallistujat:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Muut oppiaineistot +## Muut opetussuunnitelmat -Tiimimme tuottaa myös muita oppiaineistoja! Tutustu niihin: +Tiimimme tuottaa myös muita opetussuunnitelmia! Tutustu: ### LangChain -[![LangChain4j aloittelijoille](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js aloittelijoille](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Agentit -[![AZD aloittelijoille](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI aloittelijoille](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP aloittelijoille](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI Agentit aloittelijoille](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- -### Generatiivisen tekoälyn sarja -[![Generatiivinen tekoäly aloittelijoille](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generatiivinen tekoäly (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generatiivinen tekoäly (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generatiivinen tekoäly (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +### Generatiivinen AI -sarja +[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- -### Perusoppiminen -[![ML aloittelijoille](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Data science aloittelijoille](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![AI aloittelijoille](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Kyberturvallisuus aloittelijoille](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Web-kehitys aloittelijoille](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT aloittelijoille](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR-kehitys aloittelijoille](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +### Perusopetus +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Copilot-sarja -[![Copilot tekoälyn pariohjelmointiin](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot C#/.NET:lle](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot-seikkailu](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Apua saatavilla -Jos jumitut tai sinulla on kysyttävää tekoälysovellusten rakentamisesta. Liity muiden oppijoiden ja kokeneiden kehittäjien keskusteluihin MCP:stä. Se on tukevainen yhteisö, jossa kysymykset ovat tervetulleita ja tieto jaetaan vapaasti. +Jos jumitut tai sinulla on kysyttävää tekoälysovellusten rakentamisesta, liity muiden oppijoiden ja kokeneiden kehittäjien keskusteluihin MCP:stä. Se on kiinnostava yhteisö, jossa kysymyksiä saa esittää ja tietoa jaetaan vapaasti. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Jos sinulla on palautetta tuotteesta tai virheitä rakentamisen aikana, käy: +Jos sinulla on palautetta tuotteesta tai löytämiesi virheiden vuoksi vieraile: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +228,5 @@ Jos sinulla on palautetta tuotteesta tai virheitä rakentamisen aikana, käy: **Vastuuvapauslauseke**: -Tämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset saattavat sisältää virheitä tai epätarkkuuksia. Alkuperäinen asiakirja sen alkuperäiskielellä on virallinen lähde. Tärkeissä tiedoissa suositellaan ammattimaista ihmiskäännöstä. Emme ole vastuussa tämän käännöksen käytöstä johtuvista väärinymmärryksistä tai virhetulkinnoista. +Tämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, ota huomioon, että automaattikäännöksissä voi esiintyä virheitä tai epätarkkuuksia. Asiakirjan alkuperäinen versio alkuperäisellä kielellä on virallinen ja sitova lähde. Tärkeissä asioissa suositellaan ammattilaisen tekemää käännöstä. Emme vastaa mahdollisista väärinymmärryksistä tai tulkinnoista, jotka johtuvat tämän käännöksen käytöstä. \ No newline at end of file diff --git a/translations/fi/lessons/0-course-setup/how-to-run.md b/translations/fi/lessons/0-course-setup/how-to-run.md index dafa0122..3d238867 100644 --- a/translations/fi/lessons/0-course-setup/how-to-run.md +++ b/translations/fi/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# Kuinka suorittaa koodi +# Kuinka Suorittaa Koodi -Tämä opintokokonaisuus sisältää paljon suoritettavia esimerkkejä ja harjoituksia, joita haluat varmasti kokeilla. Jotta voit tehdä tämän, sinun täytyy pystyä suorittamaan Python-koodia Jupyter Notebooks -ympäristössä, joka on osa tätä opintokokonaisuutta. Sinulla on useita vaihtoehtoja koodin suorittamiseen: +Tämä opetussuunnitelma sisältää paljon suoritettavia esimerkkejä ja labroja, joita haluat todennäköisesti suorittaa. Tätä varten sinun on pystyttävä suorittamaan Python-koodia Jupyter-muistikirjoissa, jotka kuuluvat osana tätä opetussuunnitelmaa. Koodin suorittamiseen on useita vaihtoehtoja: -## Suorita paikallisesti omalla tietokoneellasi +## Suorita paikallisesti tietokoneellasi -Jos haluat suorittaa koodin paikallisesti omalla tietokoneellasi, sinun täytyy asentaa jokin versio Pythonista. Suosittelen henkilökohtaisesti asentamaan **[miniconda](https://conda.io/en/latest/miniconda.html)** - se on kevyt asennus, joka tukee `conda`-pakettien hallintaa eri Pythonin **virtuaaliympäristöille**. +Jotta voit suorittaa koodin paikallisesti tietokoneellasi, tarvitset Python-asennuksen. Yksi suositus on asentaa **[miniconda](https://conda.io/en/latest/miniconda.html)** – se on melko kevyt asennus, joka tukee `conda`-paketinhallintaa erilaisille Pythonin **virtuaaliympäristöille**. -Kun olet asentanut minicondan, sinun täytyy kloonata arkisto ja luoda virtuaaliympäristö, jota käytetään tässä kurssissa: +Kun olet asentanut minicondan, kloonaa varasto ja luo virtuaaliympäristö tätä kurssia varten: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,56 +24,57 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Visual Studio Code Python-laajennuksella +### Visual Studio Code ja Python-laajennus -Todennäköisesti paras tapa käyttää opintokokonaisuutta on avata se [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) -ohjelmassa, jossa on [Python-laajennus](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Tätä opetussuunnitelmaa on parasta käyttää avaamalla se [Visual Studio Codessa](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) yhdessä [Python-laajennuksen](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) kanssa. -> **Huomio**: Kun kloonaat ja avaat hakemiston VS Code -ohjelmassa, se ehdottaa automaattisesti Python-laajennusten asentamista. Sinun täytyy myös asentaa miniconda yllä kuvatulla tavalla. +> **Huomautus**: Kun kloonaat ja avaat hakemiston VS Codessa, se ehdottaa automaattisesti Python-laajennusten asentamista. Sinun on myös asennettava miniconda kuten yllä on kuvattu. -> **Huomio**: Jos VS Code ehdottaa arkiston avaamista kontissa, sinun täytyy hylätä tämä ja käyttää paikallista Python-asennusta. +> **Huomautus**: Jos VS Code ehdottaa varaston uudelleenavaamista säiliössä, sinun tulisi kieltäytyä tästä käyttääksesi paikallista Python-asennusta. -### Jupyter-selaimen käyttö +### Jupyterin käyttäminen selaimessa -Voit myös käyttää Jupyter-ympäristöä suoraan selaimessa omalla tietokoneellasi. Itse asiassa sekä klassinen Jupyter että Jupyter Hub tarjoavat varsin kätevän kehitysympäristön automaattisen täydennyksen, koodin korostuksen jne. kanssa. +Voit käyttää Jupyter-ympäristöä myös selaimella omalla tietokoneellasi. Sekä perinteinen Jupyter että JupyterHub tarjoavat kätevän kehitysympäristön automaattisen täydennyksen, koodin korostuksen jne. kanssa. -Jotta voit käynnistää Jupyterin paikallisesti, siirry kurssin hakemistoon ja suorita: +Käynnistääksesi Jupyterin paikallisesti, siirry kurssin hakemistoon ja suorita: ```bash jupyter notebook -``` -tai +``` +tai ```bash jupyterhub -``` -Tämän jälkeen voit navigoida mihin tahansa `.ipynb`-tiedostoon, avata sen ja aloittaa työskentelyn. +``` +Sen jälkeen voit siirtyä mihin tahansa `.ipynb`-tiedostoon, avata ne ja aloittaa työskentelyn. -### Suorittaminen kontissa +### Suorittaminen säiliössä -Vaihtoehtona Python-asennukselle voit suorittaa koodin kontissa. Koska arkistomme sisältää erityisen `.devcontainer`-kansion, joka ohjeistaa konttien rakentamista tätä arkistoa varten, VS Code tarjoaa mahdollisuuden avata koodi kontissa. Tämä vaatii Dockerin asennuksen ja on hieman monimutkaisempaa, joten suosittelemme tätä kokeneemmille käyttäjille. +Yksi vaihtoehto Python-asennukselle on suorittaa koodi säiliössä. Koska varastomme sisältää erityisen `.devcontainer`-kansion, joka ohjeistaa, miten säiliö rakennetaan tälle repositoriolle, VS Code tarjoaa mahdollisuuden avata koodin uudelleen säiliössä. Tämä vaatii Dockerin asennuksen ja on myös monimutkaisempi, joten suosittelemme tätä kokeneemmille käyttäjille. ## Suorittaminen pilvessä -Jos et halua asentaa Pythonia paikallisesti ja sinulla on pääsy pilvipalveluihin, hyvä vaihtoehto on suorittaa koodi pilvessä. Tässä on muutamia tapoja tehdä tämä: +Jos et halua asentaa Pythonia paikallisesti, mutta sinulla on pääsy joihinkin pilviresursseihin, hyvä vaihtoehto on suorittaa koodi pilvessä. Tämä onnistuu monella tavalla: -* Käyttämällä **[GitHub Codespaces](https://github.com/features/codespaces)**, joka on virtuaaliympäristö GitHubissa, ja se on käytettävissä VS Code -selaimen käyttöliittymän kautta. Jos sinulla on pääsy Codespacesiin, voit vain klikata **Code**-painiketta arkistossa, käynnistää Codespacesin ja aloittaa nopeasti. +* Käyttämällä **[GitHub Codespaces](https://github.com/features/codespaces)** -ympäristöä, joka on sinulle luotu virtuaaliympäristö GitHubissa ja johon pääsee VS Code -selaimen kautta. Jos sinulla on pääsy Codespacesiin, voit vain klikata varastossa **Code**-painiketta, aloittaa codespacen ja päästä nopeasti alkuun. +* Käyttämällä **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) tarjoaa ilmaisia pilvilaskentaresursseja ihmisille, jotka haluavat kokeilla jotain koodia GitHubissa. Etusivulla on painike, jolla voi avata repositorion Binderissä – tämä vie sinut nopeasti Binder-sivustolle, joka rakentaa taustalla säiliön ja käynnistää sinulle Jupyter-verkko-rajapinnan saumattomasti. -* Käyttämällä **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) tarjoaa ilmaisia pilvilaskentaresursseja, joiden avulla voit testata GitHubissa olevaa koodia. Etusivulla on painike, jolla voit avata arkiston Binderissa - tämä vie sinut Binder-sivustolle, joka rakentaa taustalla olevan kontin ja käynnistää Jupyterin verkkokäyttöliittymän saumattomasti. - -> **Huomio**: Binder estää pääsyn joihinkin verkkoresursseihin väärinkäytön estämiseksi. Tämä saattaa estää koodin toiminnan, joka hakee malleja ja/tai datakokonaisuuksia julkisesta Internetistä. Saatat joutua etsimään kiertotapoja. Lisäksi Binderin tarjoamat laskentaresurssit ovat melko perustasoisia, joten koulutus on hidasta, erityisesti myöhemmissä monimutkaisemmissa oppitunneissa. +> **Huomautus**: Väärinkäytösten estämiseksi Binderillä on estetty pääsy tiettyihin verkkoresursseihin. Tämä saattaa estää osaa koodista toimimasta, jos koodi hakee malleja ja/tai aineistoja julkisesta internetistä. Saatat tarvita kiertoteitä. Lisäksi Binderin tarjoamat laskentaresurssit ovat melko perustasoa, joten koulutus on hidasta, erityisesti myöhemmissä, vaativammissa leikkauksissa. ## Suorittaminen pilvessä GPU:n kanssa -Jotkut tämän opintokokonaisuuden myöhemmistä oppitunneista hyötyvät suuresti GPU-tuesta, koska muuten koulutus on tuskallisen hidasta. Tässä on muutamia vaihtoehtoja, erityisesti jos sinulla on pääsy pilveen joko [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) -palvelun tai oppilaitoksesi kautta: +Jotkut myöhemmistä tämän opetussuunnitelman oppitunneista hyötyvät suuresti GPU-tuesta. Mallin harjoittelu voi olla muuten tuskallisen hidasta. Vaihtoehtoja on muutamia, erityisesti jos sinulla on pääsy pilveen joko [Azure for Studentsin](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) kautta tai oppilaitoksesi kautta: -* Luo [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) ja yhdistä siihen Jupyterin kautta. Voit sitten kloonata arkiston suoraan koneelle ja aloittaa opiskelun. NC-sarjan virtuaalikoneet tukevat GPU:ta. +* Luo [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) ja yhdistä siihen Jupylerin kautta. Voit sitten kloonata repo suoraan koneelle ja aloittaa oppimisen. NC-sarjan virtuaalikoneissa on GPU-tuki. -> **Huomio**: Jotkut tilaukset, mukaan lukien Azure for Students, eivät tarjoa GPU-tukea oletuksena. Saatat joutua pyytämään lisä-GPU-ytimiä teknisen tuen kautta. +> **Huomautus**: Joihinkin tilauksiin, mukaan lukien Azure for Students, ei kuulu GPU-tukea oletuksena. Saatat joutua pyytämään lisä-GPU-ytimiä teknisen tuen pyynnöllä. -* Luo [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ja käytä siellä Notebook-ominaisuutta. [Tämä video](https://azure-for-academics.github.io/quickstart/azureml-papers/) näyttää, kuinka arkisto kloonataan Azure ML -muistikirjaan ja aloitetaan sen käyttö. +* Luo [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ja käytä siellä Notebook-ominaisuutta. [Tämä video](https://azure-for-academics.github.io/quickstart/azureml-papers/) näyttää, miten kloonataan repositorio Azure ML -muistikirjaan ja aloitetaan käyttö. -Voit myös käyttää Google Colabia, joka tarjoaa jonkin verran ilmaista GPU-tukea, ja ladata Jupyter Notebooks -tiedostoja sinne suorittaaksesi ne yksi kerrallaan. +Voit myös käyttää Google Colabia, joka sisältää jonkin verran ilmaista GPU-tukea, ja ladata Jupyter-muistikirjat sinne suorittaaksesi ne yksi kerrallaan. --- -**Vastuuvapauslauseke**: -Tämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulisi pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskäännöstä. Emme ole vastuussa tämän käännöksen käytöstä johtuvista väärinkäsityksistä tai virhetulkinnoista. \ No newline at end of file + +**Vastuuvapauslauseke**: +Tämä asiakirja on käännetty tekoälypohjaisella käännöspalvelulla [Co-op Translator](https://github.com/Azure/co-op-translator). Pyrimme tarkkuuteen, mutta ota huomioon, että automaattiset käännökset saattavat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäiskielellä tulee pitää virallisena lähteenä. Tärkeissä asioissa suositellaan ammattilaisen tekemää käännöstä. Emme ole vastuussa tämän käännöksen käytöstä aiheutuvista väärinymmärryksistä tai tulkinnoista. + \ No newline at end of file diff --git a/translations/fi/lessons/2-Symbolic/Animals.ipynb b/translations/fi/lessons/2-Symbolic/Animals.ipynb index 8ebd26f8..d6924714 100644 --- a/translations/fi/lessons/2-Symbolic/Animals.ipynb +++ b/translations/fi/lessons/2-Symbolic/Animals.ipynb @@ -10,21 +10,21 @@ "\n", "Esimerkki [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) -materiaalista.\n", "\n", - "Tässä esimerkissä toteutamme yksinkertaisen tietopohjaisen järjestelmän, joka määrittää eläimen fyysisten ominaisuuksien perusteella. Järjestelmä voidaan esittää seuraavalla JA-TAI-puulla (tämä on osa koko puusta, sääntöjä voidaan helposti lisätä): \n", + "Tässä esimerkissä toteutamme yksinkertaisen tietopohjaisen järjestelmän eläimen tunnistamiseksi fyysisten ominaisuuksien perusteella. Järjestelmä voidaan esittää seuraavana JA-TAI-puun osana (tämä on osa koko puuta, ja siihen voidaan helposti lisätä lisää sääntöjä):\n", "\n", - "![](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Oma asiantuntijajärjestelmämme kuori taaksepäin suuntautuvalle päättelylle\n", + "## Oma asiantuntijajärjestelmämme kuori takaapäin päättelyllä\n", "\n", - "Määritellään yksinkertainen kieli tiedon esittämiseen tuotantosääntöjen pohjalta. Käytämme Python-luokkia sääntöjen määrittämiseen. Periaatteessa olisi kolme luokkatyyppiä:\n", - "* `Ask` edustaa kysymystä, joka täytyy esittää käyttäjälle. Se sisältää joukon mahdollisia vastauksia.\n", - "* `If` edustaa sääntöä, ja se on vain syntaktinen apuväline säännön sisällön tallentamiseen.\n", - "* `AND`/`OR` ovat luokkia, jotka edustavat puun AND/OR-haaroja. Ne vain tallentavat argumenttilistan sisälleen. Koodin yksinkertaistamiseksi kaikki toiminnallisuus on määritelty yliluokassa `Content`.\n" + "Yritetään määritellä yksinkertainen kieli tietämyksen esittämiseen tuotantosääntöjen pohjalta. Käytämme Python-luokkia avainsanoina sääntöjen määrittämiseen. Periaatteessa luokkia on kolmenlaisia:\n", + "* `Ask` edustaa kysymystä, joka täytyy kysyä käyttäjältä. Se sisältää joukon mahdollisia vastauksia.\n", + "* `If` edustaa sääntöä, ja se on vain syntaktinen sokeri säännön sisällön tallentamiseen\n", + "* `AND`/`OR` ovat luokkia, jotka edustavat JA/TAI-haaroja puussa. Ne vain tallentavat argumenttilistan sisälleen. Koodin yksinkertaistamiseksi kaikki toiminnallisuus on määritelty yläluokassa `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Järjestelmässämme työmuisti sisältäisi **faktojen** listan **attribuutti-arvo pareina**. Tietokanta voidaan määritellä yhdeksi suureksi sanakirjaksi, joka yhdistää toiminnot (uudet faktat, jotka pitäisi lisätä työmuistiin) ehtoihin, ilmaistuna JA-TAI lausekkeina. Lisäksi joitakin faktoja voidaan `Kysy`ä.\n" + "Järjestelmässämme työmuisti sisältäisi luettelon **tiedoista** **ominaisuus-arvopareina**. Tietopohja voidaan määritellä yhtenä suurena sanakirjana, joka yhdistää toiminnot (uudet tiedot, jotka tulisi lisätä työmuistiin) ehtoihin, ilmaistuna JA-TAI lausekkeina. Lisäksi joitakin tietoja voidaan `Kysyä`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Määrittääksemme taaksepäin suuntautuvan päättelyn, luomme `Knowledgebase`-luokan. Se sisältää:\n", - "* Toimivan `muistin` - sanakirjan, joka yhdistää attribuutit arvoihin\n", - "* Tietokannan `säännöt` yllä määritellyssä muodossa\n", + "Taaksepäin päättelyn suorittamiseksi määrittelemme `Knowledgebase`-luokan. Se sisältää:\n", + "* Työmuistin `memory` - sanakirja, joka yhdistää attribuutit arvoihin\n", + "* Tietokannan `rules` yllä määritellyssä formaatissa\n", "\n", - "Kaksi pääasiallista menetelmää ovat:\n", - "* `get`, jolla saadaan attribuutin arvo ja suoritetaan päättely tarvittaessa. Esimerkiksi, `get('color')` hakee värin arvon (kysyy tarvittaessa ja tallentaa arvon myöhempää käyttöä varten toimivaan muistiin). Jos kysytään `get('color:blue')`, se kysyy värin ja palauttaa sitten `y`/`n` arvon riippuen väristä.\n", - "* `eval` suorittaa varsinaisen päättelyn, eli käy läpi JA/TAI-puun, arvioi alitavoitteet jne.\n" + "Kaksi päämenetelmää ovat:\n", + "* `get`, jolla saadaan attribuutin arvo, suorittaen päättely tarvittaessa. Esimerkiksi `get('color')` saa väri-kohdan arvon (se kysyy tarvittaessa ja tallentaa arvon myöhempää käyttöä varten työmuistiin). Jos kysymme `get('color:blue')`, se kysyy väriä ja palauttaa sitten arvon `y`/`n` riippuen väristä.\n", + "* `eval` suorittaa varsinaisen päättelyn, eli käy läpi JA/TAI-puun, arvioi alakohtia jne.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nyt määritellään eläintietokanta ja suoritetaan konsultaatio. Huomaa, että tämä kutsu esittää sinulle kysymyksiä. Voit vastata kirjoittamalla `y`/`n` kyllä-ei kysymyksiin tai määrittämällä numeron (0..N) pidempiin monivalintakysymyksiin.\n" + "Määritellään nyt eläintietokantamme ja suoritetaan konsultaatio. Huomaa, että tämä kutsu esittää sinulle kysymyksiä. Vastata voit kirjoittamalla `y`/`n` kyllä/ei -kysymyksiin tai valitsemalla numeron (0..N) pidempiin monivalintakysymyksiin.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnowin käyttö eteenpäin suuntautuvaan päättelyyn\n", + "## Ekspertan käyttäminen eteenpäin päin päättelyyn\n", "\n", - "Seuraavassa esimerkissä yritämme toteuttaa eteenpäin suuntautuvaa päättelyä käyttämällä yhtä tietojen esitykseen tarkoitettua kirjastoa, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** on kirjasto, joka mahdollistaa eteenpäin suuntautuvien päättelyjärjestelmien luomisen Pythonissa, ja se on suunniteltu muistuttamaan klassista vanhaa järjestelmää [CLIPS](http://www.clipsrules.net/index.html). \n", + "Seuraavassa esimerkissä yritämme toteuttaa eteenpäin päin päättelyn käyttäen yhtä tiedon esityksen kirjastoista, [Experta](https://github.com/nilp0inter/experta). **Experta** on kirjasto eteenpäin päin päättelyjärjestelmien luomiseen Pythonilla, joka on suunniteltu muistuttamaan klassista vanhaa järjestelmää [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Voisimme myös toteuttaa eteenpäin suuntautuvan päättelyn itse ilman suuria ongelmia, mutta yksinkertaiset toteutukset eivät yleensä ole kovin tehokkaita. Tehokkaampaan sääntöjen sovittamiseen käytetään erityistä algoritmia, [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Voisimme myös toteuttaa eteenpäin sarjoittamisen itse ilman suurempia ongelmia, mutta naiivit toteutukset eivät yleensä ole kovin tehokkaita. Tehokkaampaan sääntöjen sovitukseen käytetään erityistä algoritmia [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Määritämme järjestelmämme luokaksi, joka aliluokittelee `KnowledgeEngine`-luokan. Jokainen sääntö määritellään erillisellä funktiolla, jossa on `@Rule`-annotaatio, joka määrittää, milloin sääntö aktivoituu. Säännön sisällä voimme lisätä uusia faktoja käyttämällä `declare`-funktiota, ja näiden faktojen lisääminen johtaa siihen, että eteenpäin päättelevä moottori kutsuu lisää sääntöjä.\n" + "Määrittelemme järjestelmämme luokkana, joka perii `KnowledgeEngine`-luokan. Jokainen sääntö määritellään erillisenä funktiona, jossa on `@Rule`-annotaatio, joka määrittää, milloin sääntö aktivoituu. Säännön sisällä voimme lisätä uusia tietoja käyttämällä `declare`-funktiota, ja näiden tietojen lisääminen johtaa siihen, että eteenpäin päättelymoottori kutsuu lisää sääntöjä.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Kun olemme määritelleet tietokannan, täytämme työmuistin joillakin alkuperäisillä faktoilla ja kutsumme sitten `run()`-metodia suorittaaksemme päättelyn. Tuloksena näet, että uusia pääteltyjä faktoja lisätään työmuistiin, mukaan lukien lopullinen fakta eläimestä (jos olemme asettaneet kaikki alkuperäiset faktat oikein).\n" + "Kun olemme määrittäneet tietokannan, täytämme työmuistimme joillakin alkuperäisillä tosiasioilla ja kutsumme sitten `run()`-metodia suorittaaksemme päättelyn. Näet tuloksena, että uusia pääteltyjä tosiasioita lisätään työmuistiin, mukaan lukien lopullinen tosiasia eläimestä (jos olemme asettaneet kaikki alkuperäiset tosiasiat oikein).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Vastuuvapauslauseke**: \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulisi pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskäännöstä. Emme ole vastuussa väärinkäsityksistä tai virhetulkinnoista, jotka johtuvat tämän käännöksen käytöstä.\n" + "---\n\n\n**Vastuuvapauslauseke**: \nTämä asiakirja on käännetty tekoälypohjaisella käännöspalvelulla [Co-op Translator](https://github.com/Azure/co-op-translator). Pyrimme tarkkuuteen, mutta otathan huomioon, että automaattikäännöksissä saattaa esiintyä virheitä tai epätarkkuuksia. Alkuperäinen asiakirja sen alkuperäiskielellä on virallinen ja sitova lähde. Tärkeissä asioissa suositellaan ammattilaisen tekemää ihmiskäännöstä. Emme ole vastuussa tämän käännöksen käytöstä mahdollisesti aiheutuvista väärinkäsityksistä tai tulkinnoista.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T21:04:49+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T02:51:56+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "fi" } diff --git a/translations/fi/lessons/2-Symbolic/README.md b/translations/fi/lessons/2-Symbolic/README.md index a9e7ea0e..05612fc2 100644 --- a/translations/fi/lessons/2-Symbolic/README.md +++ b/translations/fi/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Tiedon esittäminen ja asiantuntijajärjestelmät -![Symbolisen tekoälyn sisällön yhteenveto](../../../../translated_images/fi/ai-symbolic.715a30cb610411a6.webp) +![Yhteenveto symbolisen tekoälyn sisällöstä](../../../../../../translated_images/fi/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote: [Tomomi Imura](https://twitter.com/girlie_mac) +> Luonnos [Tomomi Imura](https://twitter.com/girlie_mac) -Tekoälyn tavoite perustuu tiedon etsintään, pyrkimykseen ymmärtää maailmaa samalla tavalla kuin ihmiset. Mutta miten tämä voidaan toteuttaa? +Tekoälyn tavoittelu perustuu tiedon etsimiseen, jotta maailmaa voitaisiin ymmärtää samalla tavalla kuin ihmiset. Mutta miten tähän voisi ryhtyä? -## [Ennakkokysely ennen luentoa](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Esiluentokysely](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Tekoälyn alkuvaiheissa suosittiin ylhäältä alas -lähestymistapaa älykkäiden järjestelmien luomisessa (käsitelty edellisessä oppitunnissa). Ideana oli siirtää ihmisten tieto koneelle luettavassa muodossa ja käyttää sitä ongelmien automaattiseen ratkaisemiseen. Tämä lähestymistapa perustui kahteen suureen ideaan: +Tekoälyn alkuaikoina yleinen lähestymistapa älykkäiden järjestelmien luomiseksi (joka käsiteltiin edellisessä oppitunnissa) oli ylhäältä alas -menetelmä. Ajatus oli poimia tieto ihmisiltä koneen lukemassa muodossa ja käyttää sitä ongelmien automaattiseen ratkaisuun. Tämä lähestymistapa perustui kahteen suureen ideaan: * Tiedon esittäminen * Päättely ## Tiedon esittäminen -Yksi symbolisen tekoälyn tärkeistä käsitteistä on **tieto**. On tärkeää erottaa tieto *informaatiosta* tai *datasta*. Esimerkiksi voidaan sanoa, että kirjat sisältävät tietoa, koska niiden avulla voi oppia ja tulla asiantuntijaksi. Todellisuudessa kirjat sisältävät kuitenkin *dataa*, ja lukemalla kirjoja ja integroimalla tämä data maailmankuvaamme muutamme datan tiedoksi. +Symbolisen tekoälyn tärkeimpiin käsitteisiin kuuluu **tieto**. On tärkeää erottaa tieto *tiedosta* tai *datasta*. Esimerkiksi voi sanoa, että kirjat sisältävät tietoa, koska kirjoja lukemalla voi tulla asiantuntijaksi. Todellisuudessa kirjat sisältävät *dataa*, ja lukemalla ja integroimalla tätä dataa maailmankuvaamme muutamme datan tiedoksi. -> ✅ **Tieto** on jotain, joka on päässämme ja edustaa ymmärrystämme maailmasta. Se saadaan aktiivisen **oppimisprosessin** kautta, joka integroi vastaanottamamme informaation aktiiviseen maailmankuvaamme. +> ✅ **Tieto** on jotain, mikä on meidän päässämme ja edustaa ymmärrystämme maailmasta. Se saadaan aktiivisella **oppimisprosessilla**, joka integroi saamamme tiedon osat aktiiviseen maailmankuvaamme. -Usein emme määrittele tietoa tarkasti, vaan yhdistämme sen muihin siihen liittyviin käsitteisiin käyttäen [DIKW-pyramidia](https://en.wikipedia.org/wiki/DIKW_pyramid). Pyramidissa on seuraavat käsitteet: +Useimmiten emme määrittele tietoa tarkasti, vaan sovitamme sen muihin siihen liittyviin käsitteisiin käyttäen [DIKW-pyramidia](https://en.wikipedia.org/wiki/DIKW_pyramid). Se sisältää seuraavat käsitteet: -* **Data** on jotain, joka on esitetty fyysisessä muodossa, kuten kirjoitettu teksti tai puhuttu sana. Data on riippumatonta ihmisistä ja sitä voidaan siirtää henkilöltä toiselle. -* **Informaatio** on tapa, jolla tulkitsemme dataa päässämme. Esimerkiksi kun kuulemme sanan *tietokone*, meillä on jonkinlainen käsitys siitä, mitä se on. -* **Tieto** on informaatiota, joka on integroitu maailmankuvaamme. Esimerkiksi kun opimme, mitä tietokone on, alamme ymmärtää, miten se toimii, kuinka paljon se maksaa ja mihin sitä voidaan käyttää. Tämä toisiinsa liittyvien käsitteiden verkosto muodostaa tietomme. -* **Viisaus** on vielä yksi taso ymmärryksessämme maailmasta, ja se edustaa *metatietoa*, esimerkiksi käsitystä siitä, miten ja milloin tietoa tulisi käyttää. +* **Data** on jotain, joka on esitetty fyysisessä muodossa, kuten kirjoitettuna tekstinä tai puhuttuina sanoina. Data on olemassa ihmisten ulkopuolella ja voi siirtyä ihmiseltä toiselle. +* **Informaatio** on tapa, jolla tulkitsemme dataa päässämme. Esimerkiksi kuullessamme sanan *tietokone*, ymmärrämme jonkin verran, mitä se tarkoittaa. +* **Tieto** on informaation integroimista maailmankuvaamme. Esimerkiksi kun opimme, mikä tietokone on, alamme saada käsityksiä siitä, miten se toimii, kuinka paljon se maksaa ja mihin sitä voi käyttää. Tämä verkosto keskenään liittyviä käsitteitä muodostaa tietomme. +* **Viisaus** on vielä korkeampi taso ymmärryksestämme maailmasta ja se edustaa *metatietoa*, esim. käsityksen siitä, miten ja milloin tietoa tulisi käyttää. - + -*Kuva [Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*Kuva [Wikipediasta](https://commons.wikimedia.org/w/index.php?curid=37705247), tekijä Longlivetheux - oma työ, CC BY-SA 4.0* -Näin ollen **tiedon esittämisen** ongelma on löytää tehokas tapa esittää tieto tietokoneessa datan muodossa, jotta sitä voidaan käyttää automaattisesti. Tämä voidaan nähdä spektrinä: +Näin ollen **tiedon esittämisen** ongelmana on löytää jokin tehokas tapa edustaa tietoa tietokoneen sisällä datamuodossa, jotta se olisi automaattisesti hyödynnettävissä. Tätä voidaan tarkastella spektrinä: -![Tiedon esittämisen spektri](../../../../translated_images/fi/knowledge-spectrum.b60df631852c0217.webp) +![Tiedon esittämisen spektri](../../../../../../translated_images/fi/knowledge-spectrum.b60df631852c0217.webp) > Kuva: [Dmitry Soshnikov](http://soshnikov.com) -* Vasemmalla on hyvin yksinkertaisia tiedon esitysmuotoja, joita tietokoneet voivat käyttää tehokkaasti. Yksinkertaisin on algoritminen, jossa tieto esitetään tietokoneohjelmana. Tämä ei kuitenkaan ole paras tapa esittää tietoa, koska se ei ole joustava. Päässämme oleva tieto on usein ei-algoritmista. -* Oikealla on esitysmuotoja, kuten luonnollinen teksti. Se on voimakkain, mutta sitä ei voida käyttää automaattiseen päättelyyn. +* Vasemmalla on hyvin yksinkertaisia tiedon esityksen tyyppejä, joita tietokoneet voivat tehokkaasti käyttää. Yksinkertaisin on algoritminen, jossa tieto esitetään tietokoneohjelmana. Tämä ei kuitenkaan ole paras tapa esittää tietoa, koska se ei ole joustava. Tieto päässämme on usein ei-algoritmista. +* Oikealla ovat esitykset kuten luonnollinen teksti. Se on voimakkain, mutta ei sovellu automaattiseen päättelyyn. -> ✅ Mieti hetki, miten esität tietoa päässäsi ja muutat sen muistiinpanoiksi. Onko jokin tietty muoto, joka auttaa sinua muistamaan paremmin? +> ✅ Mieti hetki, miten esität tietoa päässäsi ja miten muutat sen muistiinpanoiksi. Onko sinulle jokin tietty muoto, joka tukee hyvin muistamista? -## Tietokoneen tiedon esitysmuotojen luokittelu +## Tietokoneen tiedon esittämisen luokittelu -Voimme luokitella erilaisia tietokoneen tiedon esitysmuotoja seuraaviin kategorioihin: +Voimme luokitella eri tietokoneen tiedon esitystavat seuraaviin kategorioihin: -* **Verkkoesitykset** perustuvat siihen, että päässämme on verkosto toisiinsa liittyviä käsitteitä. Voimme yrittää jäljentää samanlaisia verkostoja graafina tietokoneessa - niin sanottu **semanttinen verkko**. +* **Verkkopohjaiset esitykset** perustuvat siihen, että meillä on päässämme verkosto keskenään liittyviä käsitteitä. Voimme yrittää rekonstruoida saman verkoston graafina tietokoneessa - ns. **semanttinen verkosto**. -1. **Objekti-Attribuutti-Arvo -kolmikot** tai **attribuutti-arvo -parit**. Koska graafi voidaan esittää tietokoneessa solmujen ja kaarien listana, voimme esittää semanttisen verkon listana kolmikoita, jotka sisältävät objektit, attribuutit ja arvot. Esimerkiksi voimme rakentaa seuraavat kolmikot ohjelmointikielistä: +1. **Objekti-Attribuutti-Arvo -kolmikot** tai **attribuutti-arvoparit**. Koska graafi voidaan esittää tietokoneessa listana solmuista ja kaarista, voimme esittää semanttisen verkoston kolmikolistana, joka sisältää objektit, attribuutit ja arvot. Esimerkiksi rakennamme seuraavat kolmikot ohjelmointikielistä: Objekti | Attribuutti | Arvo ---------|------------|----- -Python | on | Tyypittämätön kieli -Python | keksijä | Guido van Rossum -Python | lohkorakenne | sisennys -Tyypittämätön kieli | ei sisällä | tyyppimäärityksiä +--------|-------------|----- +Python | on | Typetön-kieli +Python | kehittänyt | Guido van Rossum +Python | lohko-syntaksi | sisennys +Typetön-kieli | ei sisällä | tyyppimääritelmiä -> ✅ Mieti, miten kolmikoita voidaan käyttää muun tyyppisen tiedon esittämiseen. +> ✅ Mieti, miten kolmikot voidaan käyttää muiden tiedontyyppien esittämiseen. -2. **Hierarkkiset esitykset** korostavat sitä, että usein luomme hierarkian objekteista päässämme. Esimerkiksi tiedämme, että kanarialintu on lintu, ja kaikilla linnuilla on siivet. Meillä on myös käsitys siitä, minkä värisiä kanarialinnut yleensä ovat ja mikä on niiden lentonopeus. +2. **Hierarkkiset esitykset** korostavat sitä, että usein luomme päässämme hierarkian objekteista. Esimerkiksi tiedämme, että kanaria on lintu, ja kaikilla linnuilla on siivet. Meillä on myös käsitys siitä, minkä värinen kanarian yleensä on ja mikä on sen lentonopeus. - - **Kehysesitys** perustuu siihen, että jokainen objekti tai objektien luokka esitetään **kehyksenä**, joka sisältää **paikkoja**. Paikoilla voi olla oletusarvoja, arvorajoituksia tai tallennettuja proseduureja, joita voidaan kutsua paikan arvon saamiseksi. Kaikki kehykset muodostavat hierarkian, joka on samanlainen kuin objektihierarkia olio-ohjelmointikielissä. - - **Skenaariot** ovat erityinen kehysten tyyppi, joka edustaa monimutkaisia tilanteita, jotka voivat kehittyä ajan myötä. + - **Kehysesitys** perustuu siihen, että jokainen kohde tai kohdeluokka esitetään **kehyksenä**, joka sisältää **paikat**. Paikoilla voi olla oletusarvot, arvorajoitukset tai tallennettuja käsittelyohjeita, joita voidaan kutsua paikan arvon saamiseksi. Kaikki kehykset muodostavat hierarkian, joka muistuttaa olio-ohjelmointikielten olin hierarkiaa. + - **Skenaariot** ovat erityislaatuinen kehyksen laji, joka esittää monimutkaisia tilanteita, jotka voivat avautua ajan myötä. **Python** Paikka | Arvo | Oletusarvo | Väli | --------|------|------------|------| -Nimi | Python | | | -On | Tyypittämätön kieli | | | -Muuttujan tyyli | | CamelCase | | +-------|-------|------------|-------| +Nimi | Python | | | +On | Typetön-kieli | | | +Muuttujan kirjoitusasu | | CamelCase | | Ohjelman pituus | | | 5-5000 riviä | -Lohkorakenne | Sisennys | | | +Lohko-syntaksi | Sisennys | | | -3. **Proseduraaliset esitykset** perustuvat tiedon esittämiseen toimintojen listana, jotka voidaan suorittaa, kun tietty ehto täyttyy. - - Tuotantosäännöt ovat jos-niin -lauseita, jotka mahdollistavat johtopäätösten tekemisen. Esimerkiksi lääkärillä voi olla sääntö, joka sanoo, että **JOS** potilaalla on korkea kuume **TAI** korkea C-reaktiivisen proteiinin taso verikokeessa **NIIN** hänellä on tulehdus. Kun kohtaamme jonkin ehdon, voimme tehdä johtopäätöksen tulehduksesta ja käyttää sitä jatkopäättelyssä. - - Algoritmeja voidaan pitää toisena proseduraalisen esityksen muotona, vaikka niitä ei juuri koskaan käytetä suoraan tietopohjaisissa järjestelmissä. +3. **Menettelylliset esitykset** perustuvat tiedon esittämiseen toimintojen listana, joita voidaan suorittaa, kun tietty ehto täyttyy. + - Tuotantosäännöt ovat jos-niin -lauseita, jotka mahdollistavat päätelmien teon. Esimerkiksi lääkärillä voi olla sääntö, jossa **JOS** potilaalla on korkea kuume **TAI** korkea C-reaktiivisen proteiinin arvo verikokeessa, **NIIN** hänellä on tulehdus. Kun täytämme jonkin ehdon, voimme tehdä päätelmän tulehduksesta ja käyttää tätä päättelyssä eteenpäin. + - Algoritmit voidaan katsoa toiseksi menettelylliseksi esitykseksi, vaikka niitä käytetään melkein koskaan suoraan tietopohjaisissa järjestelmissä. -4. **Logiikka** ehdotettiin alun perin Aristoteleen toimesta universaalin ihmistiedon esittämiseksi. - - Predikaattilogiikka matemaattisena teoriana on liian rikas ollakseen laskettavissa, joten siitä käytetään yleensä jotakin osajoukkoa, kuten Prologissa käytettyjä Horn-lauseita. - - Kuvaileva logiikka on joukko loogisia järjestelmiä, joita käytetään hierarkioiden ja hajautettujen tiedon esitysten, kuten *semanttisen webin*, esittämiseen ja päättelyyn. +4. **Logiikka** esitettiin alun perin Aristoteleen toimesta universaalin ihmistiedon esittämiseen. + - Predikaattilogiikka matemaattisena teoriayhtenä on liian rikas laskettavaksi, siksi yleensä käytetään jotain sen osajoukkoa, kuten Prologissa käytettyjä Hornin klausuleita. + - Deskriptiivinen logiikka on loogisten järjestelmien perhe, jota käytetään hierarkioiden ja jakaantuneiden tiedon esitysten, kuten *semanttisen webin*, esittämiseen ja päättelyyn. ## Asiantuntijajärjestelmät -Symbolisen tekoälyn varhaisia menestyksiä olivat niin sanotut **asiantuntijajärjestelmät** - tietokonejärjestelmät, jotka suunniteltiin toimimaan asiantuntijana jollakin rajatulla ongelma-alueella. Ne perustuivat **tietokantaan**, joka oli kerätty yhdeltä tai useammalta ihmisasiantuntijalta, ja ne sisälsivät **päättelymoottorin**, joka suoritti päättelyä sen pohjalta. +Yksi symbolisen tekoälyn varhaisista menestyksistä olivat ns. **asiantuntijajärjestelmät** – tietokonejärjestelmät, jotka oli suunniteltu toimimaan asiantuntijana rajatussa ongelma-alueessa. Ne perustuivat **tietokantaan**, joka oli kerätty yhdeltä tai useammalta ihmisasiantuntijalta, ja ne sisälsivät **päätöksentekomoottorin**, joka suoritti jonkinlaista päättelyä sen päällä. -![Ihmisen arkkitehtuuri](../../../../translated_images/fi/arch-human.5d4d35f1bba3ab1c.webp) | ![Tietopohjaisen järjestelmän arkkitehtuuri](../../../../translated_images/fi/arch-kbs.3ec5c150b09fa8da.webp) +![Ihmisen arkkitehtuuri](../../../../../../translated_images/fi/arch-human.5d4d35f1bba3ab1c.webp) | ![Tietopohjaisen järjestelmän arkkitehtuuri](../../../../../../translated_images/fi/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Ihmisen hermojärjestelmän yksinkertaistettu rakenne | Tietopohjaisen järjestelmän arkkitehtuuri -Asiantuntijajärjestelmät rakennetaan ihmisen päättelyjärjestelmän tapaan, joka sisältää **lyhytkestoisen muistin** ja **pitkäkestoisen muistin**. Vastaavasti tietopohjaisissa järjestelmissä erotamme seuraavat komponentit: +Asiantuntijajärjestelmät rakennetaan kuten ihmisen päättelyjärjestelmä, joka sisältää **lyhytaikaisen muistin** ja **pitkäaikaisen muistin**. Samalla tavalla tietopohjaisissa järjestelmissä erotamme seuraavat komponentit: -* **Ongelman muisti**: sisältää tiedon parhaillaan ratkaistavasta ongelmasta, kuten potilaan lämpötilan tai verenpaineen, onko hänellä tulehdus vai ei jne. Tätä tietoa kutsutaan myös **staattiseksi tiedoksi**, koska se sisältää hetkellisen tilannekuvan siitä, mitä tiedämme ongelmasta - niin sanotun *ongelman tilan*. -* **Tietokanta**: edustaa pitkäkestoista tietoa ongelma-alueesta. Se kerätään manuaalisesti ihmisasiantuntijoilta eikä muutu konsultoinnista toiseen. Koska sen avulla voidaan navigoida ongelman tilasta toiseen, sitä kutsutaan myös **dynaamiseksi tiedoksi**. -* **Päättelymoottori**: ohjaa koko prosessia ongelmatilan tilassa etsimisessä, kysyy tarvittaessa käyttäjältä kysymyksiä ja löytää oikeat säännöt, joita sovelletaan kuhunkin tilaan. +* **Ongelman muisti**: sisältää tiedon ongelmasta, jota parhaillaan ratkaistaan, eli potilaan lämpötila tai verenpaine, onko hänellä tulehdusta vai ei jne. Tätä tietoa kutsutaan myös **staattiseksi tiedoksi**, koska se sisältää kuvan siitä, mitä ongelmasta tällä hetkellä tiedetään – ns. *ongelman tila*. +* **Tietokanta**: edustaa pitkäaikaista tietoa ongelma-alueesta. Se on manuaalisesti poimittu ihmisasiantuntijoilta eikä muutu konsultoinneista toisiin. Koska sen avulla voidaan navigoida ongelmatilasta toiseen, sitä kutsutaan myös **dynaamiseksi tiedoksi**. +* **Päätöksentekomoottori**: ohjaa koko prosessia ongelmatilatilan haussa, esittää käyttäjälle kysymyksiä tarvittaessa. Se on myös vastuussa sopivien sääntöjen löytämisestä, joita sovelletaan kuhunkin tilaan. -Esimerkiksi tarkastellaan seuraavaa asiantuntijajärjestelmää, joka määrittää eläimen sen fyysisten ominaisuuksien perusteella: +Esimerkkinä otetaan seuraava asiantuntijajärjestelmä eläimen tunnistamiseen sen fyysisten ominaisuuksien perusteella: -![AND-OR-puu](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR puu](../../../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.webp) > Kuva: [Dmitry Soshnikov](http://soshnikov.com) -Tätä kaaviota kutsutaan **AND-OR-puuksi**, ja se on graafinen esitys tuotantosääntöjen joukosta. Puun piirtäminen on hyödyllistä asiantuntijalta tiedon keräämisen alkuvaiheessa. Tiedon esittämiseksi tietokoneessa on kuitenkin kätevämpää käyttää sääntöjä: +Tätä kaaviota kutsutaan **AND-OR -puuksi**, ja se on graafinen esitys joukosta tuotantosääntöjä. Puun piirtäminen on hyödyllistä alussa, kun tieto poimitaan asiantuntijalta. Tiedon edustamiseksi tietokoneessa on kätevämpää käyttää sääntöjä: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Huomaat, että jokainen ehto säännön vasemmalla puolella ja toiminto ovat pohjimmiltaan objekti-attribuutti-arvo (OAV) -kolmikoita. **Työmuisti** sisältää joukon OAV-kolmikoita, jotka vastaavat parhaillaan ratkaistavaa ongelmaa. **Sääntömoottori** etsii sääntöjä, joiden ehdot täyttyvät, ja soveltaa niitä, lisäten uuden kolmikon työmuistiin. +Voit huomata, että jokainen sääntöjen vasemman puolen ehto ja toiminto ovat pohjimmiltaan objekti-attribuutti-arvo (OAV) -kolmikot. **Työmuisti** sisältää OAV-kolmikot, jotka vastaavat parhaillaan ratkaistavaa ongelmaa. **Sääntömoottori** etsii sääntöjä, joiden ehto täyttyy, ja soveltaa niitä lisäämällä uuden kolmikon työmuistiin. -> ✅ Piirrä oma AND-OR-puu jostakin sinua kiinnostavasta aiheesta! +> ✅ Kirjoita oma AND-OR -puusi aiheesta, josta pidät! -### Eteenpäin vs. taaksepäin päättely +### Eteenpäin- vs. Taaksepäin-päättely -Edellä kuvattu prosessi kutsutaan **eteenpäin päättelyksi**. Se alkaa ongelman alkuperäisistä tiedoista, jotka ovat saatavilla työmuistissa, ja suorittaa seuraavan päättelysilmukan: +Edellä kuvattu prosessi on nimeltään **eteenpäin-päättely**. Se alkaa jostain ongelman alkuperäisestä tiedosta työmuistissa ja suorittaa seuraavan päättelysilmukan: -1. Jos kohdeattribuutti on työmuistissa - lopeta ja anna tulos -2. Etsi kaikki säännöt, joiden ehdot täyttyvät - muodosta **konfliktijoukko** sääntöjä. -3. Suorita **konfliktinratkaisu** - valitse yksi sääntö, joka suoritetaan tässä vaiheessa. Konfliktinratkaisustrategioita voi olla erilaisia: - - Valitse ensimmäinen sovellettava sääntö tietokannasta +1. Jos tavoiteattribuutti on työmuistissa - lopeta ja anna tulos +2. Etsi kaikki säännöt, joiden ehto tällä hetkellä täyttyy - saadaan **konfliktijoukko** sääntöjä +3. Suorita **konfliktinratkaisu** - valitse yksi sääntö, jota sovelletaan tässä vaiheessa. Konfliktinratkaisuun voi olla erilaisia strategioita: + - Valitse ensimmäinen soveltuva sääntö tietokannasta - Valitse satunnainen sääntö - - Valitse *tarkempi* sääntö, eli se, joka täyttää eniten ehtoja vasemmalla puolella ("LHS") + - Valitse *tarkempi* sääntö, eli sellainen, joka täyttää eniten ehtoja vasemmalla puolella (LHS) 4. Sovella valittua sääntöä ja lisää uusi tieto ongelmatilaan -5. Toista vaiheesta 1. +5. Toista kohdasta 1 -Joissakin tapauksissa saatamme kuitenkin haluta aloittaa tyhjällä tiedolla ongelmasta ja esittää kysymyksiä, jotka auttavat meitä pääsemään johtopäätökseen. Esimerkiksi lääketieteellisessä diagnostiikassa emme yleensä tee kaikkia lääketieteellisiä analyyseja etukäteen ennen potilaan diagnosointia. Pikemminkin haluamme tehdä analyyseja, kun päätös täytyy tehdä. +Joissakin tapauksissa saatamme haluta aloittaa ongelmasta tyhjällä tiedolla ja esittää kysymyksiä, jotka auttavat meitä pääsemään johtopäätökseen. Esimerkiksi lääketieteellisessä diagnoosissa emme yleensä tee kaikkia tutkimuksia ennakkoon, vaan teemme ne tarpeen mukaan. -Tämä prosessi voidaan mallintaa **taaksepäin päättelyllä**. Se ohjautuu **tavoitteesta** - attribuuttiarvosta, jota etsimme: +Tästä voidaan mallintaa prosessi **taaksepäin-päättelyllä**. Se ohjautuu **tavoitteesta** – etsimämme attribuuttiarvosta: -1. Valitse kaikki säännöt, jotka voivat antaa meille tavoitteen arvon (eli tavoite oikealla puolella ("RHS")) - konfliktijoukko -1. Jos tälle attribuutille ei ole sääntöjä tai on sääntö, joka sanoo, että käyttäjältä pitäisi kysyä arvo - kysy se, muuten: -1. Käytä konfliktinratkaisustrategiaa valitaksesi yksi sääntö, jota käytämme *hypoteesina* - yritämme todistaa sen -1. Toista prosessi rekursiivisesti kaikille säännön vasemmalla puolella oleville attribuuteille, yrittäen todistaa ne tavoitteina -1. Jos prosessi epäonnistuu jossain vaiheessa - käytä toista sääntöä vaiheessa 3. +1. Valitse kaikki säännöt, jotka voivat antaa tavoitteen arvon (eli joissa tavoite on oikealla puolella (RHS)) – konfliktijoukko +1. Jos tälle attribuutille ei ole sääntöjä tai on sääntö, että arvo kysytään käyttäjältä – kysy arvo käyttäjältä, muutoin: +1. Käytä konfliktinratkaisustrategiaa valitaksesi yhden säännön, jota käytämme *hypoteesina* – yritämme todistaa sen +1. Toista rekursiivisesti prosessi kaikille säännön vasemman puolen attribuuteille tavoitteina +1. Jos prosessi epäonnistuu jossain vaiheessa – käytä toista sääntöä kohdassa 3 -> ✅ Missä tilanteissa eteenpäin päättely on sopivampaa? Entä taaksepäin päättely? +> ✅ Millaisissa tilanteissa eteenpäin-päättely on sopivampi? Entä taaksepäin-päättely? -### Asiantuntijajärjestelmien toteuttaminen +### Asiantuntijajärjestelmien toteutus -Asiantuntijajärjestelmiä voidaan toteuttaa eri työkaluilla: +Asiantuntijajärjestelmät voidaan toteuttaa erilaisin työkaluin: -* Ohjelmoimalla ne suoraan jollakin korkean tason ohjelmointikielellä. Tämä ei ole paras idea, koska tietopohjaisen järjestelmän tärkein etu on, että tieto on erotettu päättelystä, ja ongelma-alueen asiantuntijan pitäisi pystyä kirjoittamaan sääntöjä ymmärtämättä päättelyprosessin yksityiskohtia. -* Käyttämällä **asiantuntijajärjestelmän kuorta**, eli järjestelmää, joka on erityisesti suunniteltu täytettäväksi tiedolla käyttäen jotakin tiedon esityskieltä. +* Ohjelmoimalla ne suoraan jollain korkean tason ohjelmointikielellä. Tämä ei ole paras ratkaisu, koska tietopohjaisen järjestelmän tärkein etu on, että tieto on erotettu päättelystä, ja ongelma-alueen asiantuntija voisi periaatteessa kirjoittaa sääntöjä ymmärtämättä päättelyprosessin yksityiskohtia. +* Käyttämällä **asiantuntijajärjestelmäkuorta**, eli järjestelmää, joka on erityisesti suunniteltu täytettäväksi tiedolla jonkin tiedon esityskielen avulla. -## ✍️ Harjoitus: Eläinten päättely +## ✍️ Harjoitus: Eläimen tunnistus -Katso [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) esimerkki eteenpäin ja taaksepäin päättelyä käyttävän asiantuntijajärjestelmän toteuttamisesta. +Katso [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) esimerkki eteen- ja taaksepäin-päättelyä käyttävän asiantuntijajärjestelmän toteutuksesta. -> **Huomio**: Tämä esimerkki on melko yksinkertainen ja antaa vain käsityksen siitä, miltä asiantuntijajärjestelmä näyttää. Kun alat luoda tällaista järjestelmää, huomaat *älykkään* käyttäytymisen vasta, kun sääntöjen määrä saavuttaa tietyn rajan, noin 200+. Jossain vaiheessa säännöt muuttuvat liian monimutkaisiksi, jotta kaikki ne voisi pitää mielessä, ja saatat alkaa ihmetellä, miksi järjestelmä tekee tiettyjä päätöksiä. Tietopohjaisten järjestelmien tärkeä ominaisuus on kuitenkin se, että voit aina *selittää*, miten mikä tahansa päätös tehtiin. +> **Huomio**: Tämä esimerkki on melko yksinkertainen ja antaa vain käsityksen siitä, miltä asiantuntijajärjestelmä näyttää. Kun alat luoda tällaista järjestelmää, huomaat *älykkään* käyttäytymisen vasta, kun sääntöjä on käytössä noin 200+. Tällöin säännöistä tulee liian monimutkaisia muistaa kokonaisuudessaan, ja alat ihmetellä järjestelmän tekemiä päätöksiä. Tietopohjaisen järjestelmän tärkeä ominaisuus on kuitenkin se, että voit aina *selittää* tarkalleen, miten mikä tahansa päätös on tehty. ## Ontologiat ja semanttinen web -1900-luvun lopulla käynnistettiin hanke käyttää tiedon esittämistä Internet-resurssien annotointiin, jotta olisi mahdollista löytää resursseja, jotka vastaavat hyvin tarkkoja kyselyitä. Tätä liikettä kutsuttiin **semanttiseksi webiksi**, ja se perustui useisiin käsitteisiin: +20. vuosisadan lopulla käynnistettiin aloite käyttää tiedon esittämistä merkitsemään Internet-resursseja siten, että olisi mahdollista löytää hyvin spesifeihin kyselyihin vastaavia resursseja. Tätä liikettä kutsuttiin **semanttiseksi webiksi** ja se perustui useisiin käsitteisiin: -- Erityinen tiedon esitysmuoto, joka perustuu **[kuvailevaan logiikkaan](https://en.wikipedia.org/wiki/Description_logic)** (DL). Se on samanlainen kuin kehysten tiedon esitys, koska se rakentaa hierarkian objekteista ja ominaisuuksista, mutta sillä on muodollinen looginen semantiikka ja päättely. DL:stä on olemassa kokonainen perhe, joka tasapainottelee ilmaisukyvyn ja päättelyn algoritmisen monimutkaisuuden välillä. -- Hajautettu tiedon esitys, jossa kaikki käsitteet esitetään globaalilla URI-tunnisteella, mikä mahdollistaa tietohierarkioiden luomisen, jotka kattavat Internetin. +- Erityinen tiedon esitys perustuen **[deskriptiiviseen logiikkaan](https://en.wikipedia.org/wiki/Description_logic)** (DL). Se on samankaltainen kuin kehystieto, koska rakentaa hierarkian objekteista ominaisuuksineen, mutta sillä on muodolliset loogiset semantiikat ja päättely. DL:llä on kokonainen perhe, joka tasapainottaa ilmaisukykyä ja päättelyn algoritmista monimutkaisuutta. +- Hajautettu tiedon esitys, jossa kaikki käsitteet esitetään globaalilla URI-tunnisteella, mahdollistaen internetin laajuiset tiedon hierarkiat. - XML-pohjaisten kielten perhe tiedon kuvaamiseen: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Semanttisen webin keskeinen käsite on **Ontologia**. Se tarkoittaa ongelma-alueen eksplisiittistä määrittelyä käyttäen muodollista tiedon esitystä. Yksinkertaisin ontologia voi olla vain hierarkia ongelma-alueen objekteista, mutta monimutkaisemmat ontologiat sisältävät sääntöjä, joita voidaan käyttää päättelyyn. +Yksi keskeinen käsite semanttisessa webissä on **ontologia**. Se tarkoittaa eksplisiittistä ongelma-alueen määrittelyä jonkin muodollisen tiedon esityksen avulla. Yksinkertaisin ontologia voi olla pelkkä hierarkia ongelma-alueen objekteista, mutta monimutkaisemmissa ontologioissa on sääntöjä, joita voidaan käyttää päättelyyn. -Semanttisessa webissä kaikki esitykset perustuvat kolmikoihin. Jokainen objekti ja jokainen suhde tunnistetaan yksilöllisesti URI:n avulla. Esimerkiksi, jos haluamme ilmaista, että tämä AI Curriculum on kehitetty Dmitry Soshnikovin toimesta 1. tammikuuta 2022, voimme käyttää seuraavia kolmikoita: +Semanttisessa webissä kaikki esitykset perustuvat tripletteihin. Jokainen objekti ja jokainen suhde tunnistetaan yksiselitteisesti URI:n avulla. Esimerkiksi, jos haluamme ilmaista, että tämä AI Curriculum on kehittänyt Dmitry Soshnikov 1. tammikuuta 2022 – käytämme seuraavia triplettejä: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Tässä `http://www.example.com/terms/creation-date` ja `http://purl.org/dc/elements/1.1/creator` ovat tunnettuja ja yleisesti hyväksyttyjä URI:ita, jotka ilmaisevat käsitteet *luoja* ja *luomispäivämäärä*. +> ✅ Tässä `http://www.example.com/terms/creation-date` ja `http://purl.org/dc/elements/1.1/creator` ovat joitakin tunnettuja ja yleisesti hyväksyttyjä URI:ita, jotka kuvaavat käsitteitä *tekijä* ja *luontipäivä*. -Monimutkaisemmassa tapauksessa, jos haluamme määritellä luojien listan, voimme käyttää RDF:ssä määriteltyjä tietorakenteita. +Monimutkaisemmassa tapauksessa, jos haluamme määritellä listan tekijöistä, voimme käyttää RDF:ssä määriteltyjä tietorakenteita. - + > Yllä olevat kaaviot: [Dmitry Soshnikov](http://soshnikov.com) -Semanttisen webin kehitys hidastui jossain määrin hakukoneiden ja luonnollisen kielen käsittelytekniikoiden menestyksen vuoksi, jotka mahdollistavat rakenteellisen tiedon poimimisen tekstistä. Kuitenkin joillakin alueilla tehdään edelleen merkittäviä ponnisteluja ontologioiden ja tietokantojen ylläpitämiseksi. Muutamia huomionarvoisia projekteja: +Semanttisen webin rakentamisen edistyminen hidastui jossain määrin hakukoneiden ja luonnollisen kielen käsittelytekniikoiden menestyksen myötä, jotka mahdollistavat jäsennellyn tiedon poimimisen tekstistä. Kuitenkin joillakin alueilla tehdään edelleen merkittäviä ponnistuksia ontologioiden ja tietokantojen ylläpitämiseksi. Muutamia huomionarvoisia projekteja: -* [WikiData](https://wikidata.org/) on koneellisesti luettavien tietokantojen kokoelma, joka liittyy Wikipediaan. Suurin osa tiedoista on kaivettu Wikipedian *InfoBoxeista*, rakenteellisista sisällöistä Wikipedia-sivujen sisällä. Voit [kysellä](https://query.wikidata.org/) WikiDataa SPARQL:lla, semanttisen webin erityisellä kyselykielellä. Tässä on esimerkkikysely, joka näyttää ihmisten yleisimmät silmien värit: +* [WikiData](https://wikidata.org/) on koneellisesti luettava tietokantojen kokoelma, joka liittyy Wikipediaan. Suurin osa tiedoista on louhittu Wikipedia *InfoBoxeista*, Wikipedia-sivujen sisäisistä rakenteellisista sisällöistä. Voit [tehdä hakuja](https://query.wikidata.org/) wikidataan SPARQL-kyselykielellä, joka on semanttisen webin erikoiskieli. Tässä on esimerkkikysely, joka näyttää ihmisten yleisimmät silmien värit: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) on toinen WikiDatan kaltainen projekti. +* [DBpedia](https://www.dbpedia.org/) on toinen WikiDataa vastaava hanke. -> ✅ Jos haluat kokeilla omien ontologioiden rakentamista tai olemassa olevien avaamista, on olemassa erinomainen visuaalinen ontologiaeditori nimeltä [Protégé](https://protege.stanford.edu/). Lataa se tai käytä sitä verkossa. +> ✅ Jos haluat kokeilla oman ontologian rakentamista tai olemassa olevan avaamista, on olemassa erinomainen visuaalinen ontologian muokkaustyökalu nimeltä [Protégé](https://protege.stanford.edu/). Lataa se tai käytä sitä verkossa. - + -*Web Protégé -editori avoinna Romanov-suvun ontologialla. Kuvakaappaus: Dmitry Soshnikov* +*Web Protégé -editori auki Romanovien perheen ontologian kanssa. Kuvakaappaus Dmitry Soshnikov* ## ✍️ Harjoitus: Perheontologia -Katso [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) esimerkki semanttisen webin tekniikoiden käytöstä perhesuhteiden päättelyyn. Käytämme yleisessä GEDCOM-muodossa esitettyä sukupuuta ja perhesuhteiden ontologiaa rakentaaksemme graafin kaikista perhesuhteista annetulle joukolle yksilöitä. +Katso [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) esimerkki semanttisen webin tekniikoiden käytöstä perhesuhteiden päättelyssä. Otamme perheen sukupuun, joka on esitetty yleisessä GEDCOM-muodossa, ja perhesuhteiden ontologian, ja rakennamme kaavion kaikista perhesuhteista annetulle yksilöjoukolle. ## Microsoft Concept Graph -Useimmissa tapauksissa ontologiat luodaan huolellisesti käsin. On kuitenkin myös mahdollista **kaivaa** ontologioita jäsentämättömästä datasta, esimerkiksi luonnollisen kielen teksteistä. +Useimmissa tapauksissa ontologiat luodaan huolellisesti käsin. On kuitenkin mahdollista myös **louhia** ontologioita rakenteettomasta datasta, esimerkiksi luonnollisen kielen teksteistä. -Yksi tällainen yritys tehtiin Microsoft Researchin toimesta, ja sen tuloksena syntyi [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Tällainen yritys tehtiin Microsoft Researchin toimesta, mikä johti [Microsoft Concept Graphiin](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Se on suuri kokoelma entiteettejä, jotka on ryhmitelty `is-a`-perintäsuhteen avulla. Se mahdollistaa kysymyksiin vastaamisen, kuten "Mikä on Microsoft?" - vastaus voisi olla esimerkiksi "yritys todennäköisyydellä 0.87 ja brändi todennäköisyydellä 0.75". +Se on suuri joukko käsitteitä, jotka on ryhmitelty `on-tyyppiä`-perintäsuhteen avulla. Se mahdollistaa kysymyksiin kuten "Mikä on Microsoft?" vastaamisen: vastaus voi olla esimerkiksi "yritys todennäköisyydellä 0,87 ja brändi todennäköisyydellä 0,75". -Graafi on saatavilla joko REST API:n kautta tai suurena ladattavana tekstitiedostona, joka listaa kaikki entiteettiparit. +Grafi on saatavilla REST-rajapintana tai suurena ladattavana tekstinä, joka listaa kaikki käsiteparit. -## ✍️ Harjoitus: Konseptigraafi +## ✍️ Harjoitus: Käsitegrafi -Kokeile [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) -muistikirjaa nähdäksesi, kuinka voimme käyttää Microsoft Concept Graphia ryhmittelemään uutisartikkeleita useisiin kategorioihin. +Kokeile [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) -muistikirjaa nähdäksesi, kuinka voimme käyttää Microsoft Concept Graphia ryhmittelemään uutisartikkeleita eri kategorioihin. ## Yhteenveto -Nykyään tekoälyä pidetään usein synonyymina *koneoppimiselle* tai *neuroverkoille*. Kuitenkin ihminen osoittaa myös eksplisiittistä päättelyä, mikä on jotain, mitä neuroverkot eivät tällä hetkellä käsittele. Todellisissa projekteissa eksplisiittistä päättelyä käytetään edelleen tehtävissä, jotka vaativat selityksiä tai järjestelmän käyttäytymisen muokkaamista hallitulla tavalla. +Nykyään tekoäly mielletään usein lähinnä *koneoppimisen* tai *neuroverkkojen* synonyymiksi. Ihmisellä on kuitenkin myös eksplisiittinen päättelykyky, jota neuroverkot eivät vielä käsittele. Käytännön projekteissa eksplisiittistä päättelyä käytetään edelleen tehtävissä, jotka vaativat selityksiä tai järjestelmän käyttäytymisen hallittua muuttamista. ## 🚀 Haaste -Perheontologia-muistikirjassa, joka liittyy tähän oppituntiin, on mahdollisuus kokeilla muita perhesuhteita. Yritä löytää uusia yhteyksiä ihmisten välillä sukupuussa. +Perheontologian muistikirjassa tämän oppitunnin yhteydessä on mahdollisuus kokeilla muita perhesuhteita. Yritä löytää uusia yhteyksiä ihmisten välillä sukupuussa. -## [Luennon jälkeinen kysely](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Luentojälkeinen tietovisa](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Katsaus & Itseopiskelu +## Kertaaminen & Itsenäinen opiskelu -Tutki internetistä alueita, joissa ihmiset ovat yrittäneet kvantifioida ja koodata tietoa. Tutustu Bloom'n taksonomiaan ja palaa historiaan oppiaksesi, kuinka ihmiset ovat yrittäneet ymmärtää maailmaansa. Tutki Linnaeuksen työtä organismien taksonomian luomiseksi ja tarkastele, kuinka Dmitri Mendelejev loi tavan kuvailla ja ryhmitellä kemiallisia alkuaineita. Mitä muita mielenkiintoisia esimerkkejä löydät? +Tee hieman tutkimusta internetissä ja tutustu alueisiin, joissa ihmiset ovat yrittäneet kvantifioida ja koodata tietoa. Tutustu Bloomin taksonomiaan ja palaa historiaan oppiaksesi, kuinka ihmiset ovat yrittäneet ymmärtää maailmaansa. Tutki Linnaeuksen työtä eliöiden taksonomian luomiseksi ja seuraa, miten Dmitri Mendeleev loi tavan kemiallisten alkuaineiden kuvaamiseen ja ryhmittelyyn. Mitä muita mielenkiintoisia esimerkkejä löydät? **Tehtävä**: [Rakenna ontologia](assignment.md) --- + +**Vastuuvapauslauseke**: +Tämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattisissa käännöksissä saattaa esiintyä virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen omalla kielellä tulee pitää virallisena lähteenä. Tärkeässä tiedossa suositellaan ammattimaista ihmiskääntäjän tekemää käännöstä. Emme ota vastuuta tämän käännöksen käytöstä mahdollisesti aiheutuvista väärinymmärryksistä tai tulkinnoista. + \ No newline at end of file diff --git a/translations/nl/README.md b/translations/nl/README.md index e7b1d633..0c856ba5 100644 --- a/translations/nl/README.md +++ b/translations/nl/README.md @@ -1,8 +1,8 @@ -[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](./README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) +[Arabisch](../ar/README.md) | [Bengaals](../bn/README.md) | [Bulgaars](../bg/README.md) | [Birmaans (Myanmar)](../my/README.md) | [Chinees (Vereenvoudigd)](../zh/README.md) | [Chinees (Traditioneel, Hong Kong)](../hk/README.md) | [Chinees (Traditioneel, Macau)](../mo/README.md) | [Chinees (Traditioneel, Taiwan)](../tw/README.md) | [Kroatisch](../hr/README.md) | [Tsjechisch](../cs/README.md) | [Deens](../da/README.md) | [Nederlands](./README.md) | [Ests](../et/README.md) | [Fins](../fi/README.md) | [Frans](../fr/README.md) | [Duits](../de/README.md) | [Grieks](../el/README.md) | [Hebreeuws](../he/README.md) | [Hindi](../hi/README.md) | [Hongaars](../hu/README.md) | [Indonesisch](../id/README.md) | [Italiaans](../it/README.md) | [Japans](../ja/README.md) | [Kannada](../kn/README.md) | [Koreaans](../ko/README.md) | [Litouws](../lt/README.md) | [Maleis](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalees](../ne/README.md) | [Nigeriaans Pidgin](../pcm/README.md) | [Noors](../no/README.md) | [Perzisch (Farsi)](../fa/README.md) | [Pools](../pl/README.md) | [Portugees (Brazilië)](../br/README.md) | [Portugees (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Roemeens](../ro/README.md) | [Russisch](../ru/README.md) | [Servisch (Cyrillisch)](../sr/README.md) | [Slowaaks](../sk/README.md) | [Sloveens](../sl/README.md) | [Spaans](../es/README.md) | [Swahili](../sw/README.md) | [Zweeds](../sv/README.md) | [Tagalog (Filipijns)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thais](../th/README.md) | [Turks](../tr/README.md) | [Oekraïens](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamees](../vi/README.md) -> **Liever lokaal klonen?** +> **Lieber lokaal klonen?** -> Deze repository bevat meer dan 50 taalvertalingen, wat de downloadgrootte aanzienlijk vergroot. Om zonder vertalingen te klonen, gebruik sparse checkout: +> Deze repository bevat 50+ vertalingen, wat de downloadgrootte aanzienlijk vergroot. Gebruik sparse checkout om zonder vertalingen te klonen: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners @@ -47,98 +47,98 @@ Ontdek de wereld van **Kunstmatige Intelligentie** (AI) met ons 12-weken, 24-les > Dit geeft je alles wat je nodig hebt om de cursus te voltooien met een veel snellere download. -**Als je aanvullende vertalingen wenst, zijn ondersteunde talen hier vermeld [hier](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Als je extra vertalingen wilt laten ondersteunen, zijn de ondersteunde talen [hier](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) vermeld** -## Word lid van de community +## Word lid van de Community [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Wat je zult leren -**[Mindmap van de cursus](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mindmap van de Cursus](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** In dit curriculum leer je: -* Verschillende benaderingen van Kunstmatige Intelligentie, inclusief de "goede oude" symbolische benadering met **Kennisrepresentatie** en redeneren ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* Verschillende benaderingen van Kunstmatige Intelligentie, inclusief de "goede oude" symbolische aanpak met **Kennisrepresentatie** en redeneren ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). * **Neurale Netwerken** en **Deep Learning**, die de kern vormen van moderne AI. We zullen de concepten achter deze belangrijke onderwerpen illustreren met code in twee van de populairste frameworks - [TensorFlow](http://Tensorflow.org) en [PyTorch](http://pytorch.org). -* **Neuronale Architecturen** voor het werken met beelden en tekst. We behandelen recente modellen maar kunnen iets minder up-to-date zijn met de nieuwste ontwikkelingen. -* Minder populaire AI-benaderingen, zoals **Genetische Algoritmen** en **Multi-Agentensystemen**. +* **Neurale Architecturen** voor het werken met afbeeldingen en tekst. We behandelen recente modellen maar misschien niet de allernieuwste. +* Minder populaire AI-benaderingen, zoals **Genetische Algoritmen** en **Multi-Agent Systemen**. Wat we niet behandelen in dit curriculum: -> [Vind alle extra bronnen voor deze cursus in onze Microsoft Learn-collectie](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Vind alle aanvullende bronnen voor deze cursus in onze Microsoft Learn collectie](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Business cases voor het gebruik van **AI in Business**. Overweeg het volgen van het leerpad [Introductie tot AI voor zakelijke gebruikers](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) op Microsoft Learn, of [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), ontwikkeld in samenwerking met [INSEAD](https://www.insead.edu/). -* **Klassieke Machine Learning**, die goed wordt beschreven in ons [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). -* Praktische AI-toepassingen gebouwd met **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Hiervoor raden we aan te starten met Microsoft Learn-modules voor [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natuurlijke taalverwerking](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generatieve AI met Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** en andere. -* Specifieke ML **Cloud Frameworks**, zoals [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), of [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Overweeg de leerpaden [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) en [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Conversational AI** en **Chatbots**. Er is een apart leerpad [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), en je kunt ook [deze blogpost](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) raadplegen voor meer details. -* **Diepe wiskunde** achter deep learning. Hiervoor raden we [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) aan van Ian Goodfellow, Yoshua Bengio en Aaron Courville, die ook online beschikbaar is op [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Business cases voor het gebruik van **AI in Bedrijven**. Overweeg de leerlijn [Introductie tot AI voor zakelijke gebruikers](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) op Microsoft Learn, of [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), ontwikkeld in samenwerking met [INSEAD](https://www.insead.edu/). +* **Klassieke Machine Learning**, die goed wordt beschreven in ons [Machine Learning voor Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). +* Praktische AI-toepassingen gebouwd met **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Hiervoor raden we aan te starten met Microsoft Learn modules voor [visie](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natuurlijke taalverwerking](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI met Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** en andere. +* Specifieke ML **Cloud Frameworks**, zoals [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), of [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Overweeg gebruik te maken van de leerlijnen [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) en [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Conversational AI** en **Chatbots**. Er is een aparte leerlijn [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), en je kunt ook verwijzen naar [deze blogpost](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) voor meer detail. +* **Diepe Wiskunde** achter deep learning. Hiervoor raden we [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) aan van Ian Goodfellow, Yoshua Bengio en Aaron Courville, dat ook online beschikbaar is op [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Voor een zachte introductie tot _AI in de Cloud_-onderwerpen overweeg je het leerpad [Aan de slag met kunstmatige intelligentie op Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Voor een zachte introductie tot _AI in de Cloud_-onderwerpen kun je overwegen de leerlijn [Starten met kunstmatige intelligentie op Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) te volgen. # Inhoud -| | Les Link | PyTorch/Keras/TensorFlow | Lab | +| | Leslink | PyTorch/Keras/TensorFlow | Lab | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Cursus Setup](./lessons/0-course-setup/setup.md) | [Stel je ontwikkelomgeving in](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Cursusconfiguratie](./lessons/0-course-setup/setup.md) | [Stel je ontwikkelomgeving in](./lessons/0-course-setup/how-to-run.md) | | | I | [**Introductie tot AI**](./lessons/1-Intro/README.md) | | | | 01 | [Introductie en Geschiedenis van AI](./lessons/1-Intro/README.md) | - | - | | II | **Symbolische AI** | | 02 | [Kennisrepresentatie en Expert Systemen](./lessons/2-Symbolic/README.md) | [Expert Systemen](./lessons/2-Symbolic/Animals.ipynb) / [Ontologie](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Conceptgrafiek](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introductie tot Neurale Netwerken**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Meerlaagse Perceptron en het creëren van ons eigen Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 04 | [Multi-Layered Perceptron en het maken van ons eigen Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [Introductie tot Frameworks (PyTorch/TensorFlow) en Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**Computer Vision**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Verken Computer Vision op Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Introductie tot Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Convolutionele Neurale Netwerken](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Architecturen](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 07 | [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Architectures](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | | 08 | [Voorgetrainde Netwerken en Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) en [Training Tricks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoencoders en VAE's](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generative Adversarial Networks & Artistieke Stijl Transfer](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 09 | [Auto-encoders en VAE’s](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Generatieve Tegenstrijdige Netwerken & Artistieke Stijltransfer](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Objectdetectie](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [Semantische Segmentatie. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| 12 | [Semantische segmentatie. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**Natuurlijke Taalverwerking**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Verken Natuurlijke Taalverwerking op Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Tekstrepresentatie. BoW/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 13 | [Tekstreprentatie. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | | 14 | [Semantische woordembeddings. Word2Vec en GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Taalkundige Modellen. Je eigen embeddings trainen](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Recurrente Neurale Netwerken](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 15 | [Taalmodellering. Train je eigen embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Recurrent Neural Networks](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [Generatieve Recurrente Netwerken](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [Named Entity Recognition](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Grote taalmodellen, promptprogrammering en few-shot taken](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 20 | [Grote Taalmodellen, Prompt Programmeren en Few-Shot Taken](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Andere AI-technieken** || | -| 21 | [Genetische algoritmen](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 21 | [Genetische Algoritmen](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Deep Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Multi-Agentsystemen](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 23 | [Multi-Agent Systemen](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI-ethiek** | | | -| 24 | [AI-ethiek en Verantwoordelijke AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Verantwoordelijke AI-principes](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **Extra's** | | | -| 25 | [Multi-modale netwerken, CLIP en VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 24 | [AI-ethiek en Verantwoordelijke AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principes voor Verantwoordelijke AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **Extras** | | | +| 25 | [Multi-Modal Netwerken, CLIP en VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Elke les bevat * Voorleesmateriaal -* Uitvoerbare Jupyter Notebooks, die vaak specifiek zijn voor het framework (**PyTorch** of **TensorFlow**). Het uitvoerbare notebook bevat ook veel theoretisch materiaal, dus om het onderwerp te begrijpen moet je minstens één versie van het notebook doornemen (PyTorch of TensorFlow). -* **Labs** beschikbaar voor sommige onderwerpen, die je de mogelijkheid geven om het geleerde materiaal toe te passen op een specifieke probleemstelling. +* Uitvoerbare Jupyter Notebooks, die vaak specifiek zijn voor het framework (**PyTorch** of **TensorFlow**). Het uitvoerbare notebook bevat ook veel theoretisch materiaal, dus om het onderwerp te begrijpen moet je minstens één versie van het notebook doorlopen (ofwel PyTorch of TensorFlow). +* **Labs** beschikbaar voor sommige onderwerpen, die je de mogelijkheid geven om het geleerde materiaal toe te passen op een specifiek probleem. * Sommige secties bevatten links naar [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modules die gerelateerde onderwerpen behandelen. ## Aan de slag ### 🎯 Nieuw in AI? Begin hier! -Als je helemaal nieuw bent in AI en snel praktische voorbeelden wilt zien, bekijk dan onze [**Beginner-Friendly Examples**](./examples/README.md)! Deze bevatten: +Als je helemaal nieuw bent in AI en snel praktische voorbeelden wilt, bekijk dan onze [**Beginnersvriendelijke Voorbeelden**](./examples/README.md)! Deze omvatten: - 🌟 **Hallo AI Wereld** - Je eerste AI-programma (patroonherkenning) - 🧠 **Eenvoudig Neuraal Netwerk** - Bouw een neuraal netwerk vanaf nul -- 🖼️ **Beeldclassificatie** - Classificeer afbeeldingen met gedetailleerde commentaar +- 🖼️ **Beeldclassificatie** - Beelden classificeren met gedetailleerde uitleg - 💬 **Tekst Sentiment** - Analyseer positieve/negatieve tekst Deze voorbeelden zijn ontworpen om je te helpen AI-concepten te begrijpen voordat je aan het volledige curriculum begint. ### 📚 Volledige Curriculum Setup -- We hebben een [opstaples](./lessons/0-course-setup/setup.md) gemaakt om je te helpen met het opzetten van je ontwikkelomgeving. - Voor docenten hebben we ook een [curriculum setup les](./lessons/0-course-setup/for-teachers.md) gemaakt! -- Hoe je de [code kunt uitvoeren in VSCode of een Codepace](./lessons/0-course-setup/how-to-run.md) +- We hebben een [opstartles](./lessons/0-course-setup/setup.md) gemaakt om je te helpen met het opzetten van je ontwikkelomgeving. - Voor docenten hebben we ook een [curriculum setup-les](./lessons/0-course-setup/for-teachers.md) gemaakt! +- Hoe je [de code draait in VSCode of een Codespace](./lessons/0-course-setup/how-to-run.md) Volg deze stappen: @@ -146,51 +146,51 @@ Fork de Repository: Klik op de knop "Fork" rechtsboven op deze pagina. Clone de Repository: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Vergeet niet om deze repo te voorzien van een ster (🌟) zodat je hem later gemakkelijker terugvindt. +Vergeet niet deze repo te voorzien van een ster (🌟) zodat je hem later gemakkelijker terugvindt. ## Ontmoet andere Leerlingen -Word lid van onze [officiële AI Discord-server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) om andere cursisten die deze cursus volgen te ontmoeten, netwerken en ondersteuning te krijgen. +Word lid van onze [officiële AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) om andere leerlingen die deze cursus volgen te ontmoeten en netwerken en ondersteuning te krijgen. -Als je productfeedback of vragen hebt tijdens het bouwen, bezoek dan onze [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Als je productfeedback of vragen hebt tijdens het bouwen, bezoek dan ons [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Quizzen -> **Een opmerking over quizzen**: Alle quizzen bevinden zich in de Quiz-app map in etc\quiz-app, of [Online Hier](https://ff-quizzes.netlify.app/) Ze zijn gekoppeld vanaf binnen de lessen, de quiz app kan lokaal worden uitgevoerd of naar Azure worden gedeployed; volg de instructies in de `quiz-app` map. Ze worden geleidelijk gelokaliseerd. +> **Een opmerking over quizzen**: Alle quizzen bevinden zich in de Quiz-app map in etc\quiz-app, of [Online Hier](https://ff-quizzes.netlify.app/) Ze zijn gekoppeld vanuit de lessen; de quiz-app kan lokaal worden uitgevoerd of naar Azure worden gedeployed; volg de instructies in de `quiz-app` map. Ze worden geleidelijk gelokaliseerd. ## Hulp Gevraagd -Heb je suggesties of fouten in spelling of code gevonden? Dien een issue in of maak een pull request aan. +Heb je suggesties of spelling- of codefouten gevonden? Maak een issue aan of doe een pull request. ## Speciale Dank * **✍️ Hoofdauteur:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 Sketchnote illustrator:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Quizmaker:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **✅ Quiz Maker:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Kernbijdragers:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Andere Curriculum +## Andere Curricula -Ons team maakt ook andere curricula! Bekijk: +Ons team produceert andere curricula! Bekijk: ### LangChain -[![LangChain4j voor Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js voor Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Agents -[![AZD voor Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI voor Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP voor Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI Agents voor Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Generatieve AI Serie -[![Generative AI voor Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) [![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) @@ -198,35 +198,35 @@ Ons team maakt ook andere curricula! Bekijk: --- ### Kernleren -[![ML voor Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Data Science voor Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![AI voor Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Cybersecurity voor Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Web Dev voor Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT voor Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR Development voor Beginners](https://img.shields.io/badge/XR%20Development%20voor%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Copilot Serie -[![Copilot voor AI Gecombineerd Programmeren](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot voor C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot Avontuur](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Hulp Krijgen -Als je vastloopt of vragen hebt over het bouwen van AI-apps. Doe mee met medecursisten en ervaren ontwikkelaars in discussies over MCP. Het is een ondersteunende community waar vragen welkom zijn en kennis vrij wordt gedeeld. +Als je vastloopt of vragen hebt over het bouwen van AI-apps. Doe mee met mede-leerlingen en ervaren ontwikkelaars in discussies over MCP. Het is een ondersteunende gemeenschap waar vragen welkom zijn en kennis vrij wordt gedeeld. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Als je productfeedback of fouten tegenkomt tijdens het bouwen, bezoek: +Als je productfeedback of fouten tegenkomt tijdens het bouwen, bezoek dan: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Disclaimer**: -Dit document is vertaald met behulp van de AI-vertalingsdienst [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, dient u er rekening mee te houden dat automatische vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het oorspronkelijke document in de oorspronkelijke taal moet als de gezaghebbende bron worden beschouwd. Voor cruciale informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor enige misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling. +**Disclaimer**: +Dit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel wij streven naar nauwkeurigheid, dient u er rekening mee te houden dat automatische vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het oorspronkelijke document in de oorspronkelijke taal dient als de gezaghebbende bron te worden beschouwd. Voor cruciale informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor enige misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling. \ No newline at end of file diff --git a/translations/nl/lessons/0-course-setup/how-to-run.md b/translations/nl/lessons/0-course-setup/how-to-run.md index e2c8b5bb..28492b03 100644 --- a/translations/nl/lessons/0-course-setup/how-to-run.md +++ b/translations/nl/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Hoe de code uit te voeren -Deze cursus bevat veel uitvoerbare voorbeelden en labs die je wilt uitvoeren. Om dit te doen, moet je de mogelijkheid hebben om Python-code uit te voeren in Jupyter Notebooks die als onderdeel van deze cursus worden geleverd. Er zijn verschillende opties om de code uit te voeren: +Dit curriculum bevat veel uitvoerbare voorbeelden en labs die je wilt uitvoeren. Om dit te doen, heb je de mogelijkheid nodig om Python-code uit te voeren in Jupyter Notebooks die als onderdeel van dit curriculum worden meegeleverd. Je hebt verschillende opties om de code uit te voeren: -## Lokaal uitvoeren op je computer +## Lokaal op je computer uitvoeren -Om de code lokaal op je computer uit te voeren, moet je een versie van Python geïnstalleerd hebben. Ik raad persoonlijk aan om **[miniconda](https://conda.io/en/latest/miniconda.html)** te installeren - het is een vrij lichte installatie die de `conda` pakketbeheerder ondersteunt voor verschillende Python **virtuele omgevingen**. +Om de code lokaal op je computer uit te voeren, is een Python-installatie nodig. Een aanbeveling is om **[miniconda](https://conda.io/en/latest/miniconda.html)** te installeren - het is een vrij lichte installatie die de `conda` package manager ondersteunt voor verschillende Python **virtuele omgevingen**. -Nadat je miniconda hebt geïnstalleerd, moet je de repository klonen en een virtuele omgeving maken die voor deze cursus wordt gebruikt: +Nadat je miniconda hebt geïnstalleerd, clone je de repository en maak je een virtuele omgeving aan die voor deze cursus gebruikt wordt: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,17 +26,17 @@ conda activate ai4beg ### Visual Studio Code gebruiken met Python-extensie -Waarschijnlijk is de beste manier om de cursus te gebruiken deze te openen in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) met de [Python-extensie](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Dit curriculum gebruik je het beste wanneer je het opent in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) met de [Python-extensie](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Opmerking**: Zodra je de directory in VS Code kloont en opent, zal het automatisch voorstellen om Python-extensies te installeren. Je moet ook miniconda installeren zoals hierboven beschreven. +> **Opmerking**: Zodra je de directory clonet en opent in VS Code, wordt je automatisch voorgesteld om de Python-extensies te installeren. Je moet ook miniconda installeren zoals hierboven beschreven. -> **Opmerking**: Als VS Code je voorstelt om de repository in een container opnieuw te openen, moet je dit weigeren om de lokale Python-installatie te gebruiken. +> **Opmerking**: Als VS Code je voorstelt om de repository opnieuw te openen in een container, moet je dit weigeren om de lokale Python-installatie te gebruiken. ### Jupyter in de browser gebruiken -Je kunt ook de Jupyter-omgeving rechtstreeks vanuit de browser op je eigen computer gebruiken. Zowel klassieke Jupyter als Jupyter Hub bieden een vrij handige ontwikkelomgeving met automatische aanvulling, code-highlighting, enz. +Je kunt ook een Jupyter-omgeving gebruiken vanuit de browser op je eigen computer. Zowel klassieke Jupyter als JupyterHub bieden een handige ontwikkelomgeving met automatische aanvulling, syntaxmarkering, etc. -Om Jupyter lokaal te starten, ga naar de directory van de cursus en voer uit: +Om Jupyter lokaal te starten, ga je naar de directory van de cursus, en voer je uit: ```bash jupyter notebook @@ -45,34 +45,36 @@ of ```bash jupyterhub ``` -Je kunt dan naar een van de `.ipynb`-bestanden navigeren, ze openen en beginnen met werken. +Je kunt dan naar een van de `.ipynb`-bestanden navigeren, deze openen en beginnen met werken. ### Uitvoeren in een container -Een alternatief voor een Python-installatie is het uitvoeren van de code in een container. Omdat onze repository een speciale `.devcontainer`-map bevat die instructies geeft over hoe een container voor deze repo te bouwen, zal VS Code je aanbieden om de code in een container opnieuw te openen. Dit vereist een Docker-installatie en is ook wat complexer, dus we raden dit aan voor meer ervaren gebruikers. +Een alternatief voor Python-installatie zou zijn om de code in een container uit te voeren. Omdat onze repository een speciale `.devcontainer` map aanlevert die instructies bevat over hoe een container voor deze repo te bouwen, biedt VS Code de mogelijkheid om de code opnieuw in een container te openen. Dit vereist een Docker-installatie en is ook wat complexer, daarom raden we dit aan voor meer ervaren gebruikers. ## Uitvoeren in de cloud -Als je Python niet lokaal wilt installeren en toegang hebt tot enkele cloudresources, is een goed alternatief om de code in de cloud uit te voeren. Er zijn verschillende manieren waarop je dit kunt doen: +Als je Python niet lokaal wilt installeren, en toegang hebt tot wat cloudresources, is een goed alternatief om de code in de cloud uit te voeren. Er zijn verschillende manieren om dit te doen: -* Gebruik **[GitHub Codespaces](https://github.com/features/codespaces)**, een virtuele omgeving die voor je wordt gecreëerd op GitHub, toegankelijk via de browserinterface van VS Code. Als je toegang hebt tot Codespaces, kun je gewoon op de **Code**-knop in de repo klikken, een codespace starten en binnen no-time aan de slag gaan. -* Gebruik **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) biedt gratis computermiddelen in de cloud voor mensen zoals jij om wat code op GitHub te testen. Er is een knop op de startpagina om de repository in Binder te openen - dit zou je snel naar de Binder-site moeten brengen, die de onderliggende container bouwt en naadloos de Jupyter-webinterface start. +* Gebruik maken van **[GitHub Codespaces](https://github.com/features/codespaces)**, wat een virtuele omgeving is die voor jou op GitHub wordt aangemaakt, toegankelijk via een VS Code-browserinterface. Als je toegang hebt tot Codespaces, kun je gewoon op de **Code** knop in de repo klikken, een codespace starten en zonder vertraging aan de slag gaan. +* Gebruik maken van **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) biedt gratis computerresources in de cloud voor mensen zoals jij om code op GitHub uit te proberen. Op de startpagina is een knop om de repository in Binder te openen - dit brengt je snel naar de Binder-site, die een onderliggende container bouwt en naadloos een Jupyter-webinterface voor je start. -> **Opmerking**: Om misbruik te voorkomen, heeft Binder toegang tot sommige webresources geblokkeerd. Dit kan voorkomen dat sommige code werkt die modellen en/of datasets van het openbare internet haalt. Je moet mogelijk enkele alternatieven vinden. Bovendien zijn de computermiddelen die door Binder worden geleverd vrij basaal, dus training zal traag zijn, vooral in latere, meer complexe lessen. +> **Opmerking**: Om misbruik te voorkomen, heeft Binder toegang tot sommige webresources geblokkeerd. Dit kan voorkomen dat sommige code werkt die modellen en/of datasets van het openbare internet haalt. Mogelijk moet je enkele omwegen vinden. Ook zijn de computerresources van Binder behoorlijk beperkt, dus het trainen zal traag zijn, vooral in latere, meer complexe lessen. ## Uitvoeren in de cloud met GPU -Sommige van de latere lessen in deze cursus profiteren enorm van GPU-ondersteuning, omdat training anders extreem traag zal zijn. Er zijn een paar opties die je kunt volgen, vooral als je toegang hebt tot de cloud via [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) of via je instelling: +Sommige van de latere lessen in dit curriculum profiteren aanzienlijk van GPU-ondersteuning. Modeltraining kan anders erg traag zijn. Er zijn een paar opties die je kunt volgen, vooral als je toegang hebt tot de cloud, bijvoorbeeld via [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) of via je instelling: -* Maak een [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) en verbind ermee via Jupyter. Je kunt dan de repo rechtstreeks op de machine klonen en beginnen met leren. NC-serie VM's hebben GPU-ondersteuning. +* Maak een [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) aan en verbind ermee via Jupyter. Je kunt de repo dan direct op de machine clonen en beginnen met leren. NC-series VM's hebben GPU-ondersteuning. -> **Opmerking**: Sommige abonnementen, waaronder Azure for Students, bieden standaard geen GPU-ondersteuning. Je moet mogelijk extra GPU-cores aanvragen via een technisch ondersteuningsverzoek. +> **Opmerking**: Sommige abonnementen, waaronder Azure for Students, bieden standaard geen GPU-ondersteuning. Je moet mogelijk extra GPU-cores aanvragen via een technische supportaanvraag. -* Maak een [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) en gebruik vervolgens de Notebook-functie daar. [Deze video](https://azure-for-academics.github.io/quickstart/azureml-papers/) laat zien hoe je een repository kunt klonen in een Azure ML-notebook en ermee aan de slag kunt gaan. +* Maak een [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) aan en gebruik dan de Notebook-functie daar. [Deze video](https://azure-for-academics.github.io/quickstart/azureml-papers/) laat zien hoe je een repository in een Azure ML-notebook clonet en gebruikt. -Je kunt ook Google Colab gebruiken, dat enige gratis GPU-ondersteuning biedt, en Jupyter Notebooks daar uploaden om ze één voor één uit te voeren. +Je kunt ook Google Colab gebruiken, dat enige gratis GPU-ondersteuning heeft, en Jupyter Notebooks daar uploaden om ze stuk voor stuk uit te voeren. --- -**Disclaimer**: -Dit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, dient u zich ervan bewust te zijn dat geautomatiseerde vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal moet worden beschouwd als de gezaghebbende bron. Voor cruciale informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling. \ No newline at end of file + +**Disclaimer**: +Dit document is vertaald met behulp van de AI-vertalingsdienst [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, dient u er rekening mee te houden dat automatische vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal dient als de gezaghebbende bron te worden beschouwd. Voor cruciale informatie wordt een professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling. + \ No newline at end of file diff --git a/translations/nl/lessons/2-Symbolic/Animals.ipynb b/translations/nl/lessons/2-Symbolic/Animals.ipynb index f1021abe..901268b8 100644 --- a/translations/nl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/nl/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Implementeren van een Dieren Expert Systeem\n", + "# Implementeren van een Expert Systeem voor Dieren\n", "\n", - "Een voorbeeld uit [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", + "Een voorbeeld uit de [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "In dit voorbeeld implementeren we een eenvoudig kennisgebaseerd systeem om een dier te bepalen op basis van enkele fysieke kenmerken. Het systeem kan worden weergegeven door de volgende AND-OR-boom (dit is een deel van de volledige boom, we kunnen eenvoudig meer regels toevoegen):\n", + "In dit voorbeeld implementeren we een eenvoudig kennisgebaseerd systeem om een dier te bepalen op basis van enkele fysieke kenmerken. Het systeem kan worden weergegeven door de volgende AND-OR-boom (dit is een deel van de hele boom, we kunnen gemakkelijk meer regels toevoegen):\n", "\n", - "![](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Onze eigen expertensysteem-shell met achterwaartse inferentie\n", + "## Onze eigen expert systeemomgeving met achterwaartse inferentie\n", "\n", - "Laten we proberen een eenvoudige taal te definiëren voor kennisrepresentatie op basis van productieregels. We zullen Python-klassen gebruiken als trefwoorden om regels te definiëren. Er zijn in wezen drie soorten klassen:\n", - "* `Ask` vertegenwoordigt een vraag die aan de gebruiker moet worden gesteld. Het bevat de set mogelijke antwoorden.\n", - "* `If` vertegenwoordigt een regel en is slechts syntactische suiker om de inhoud van de regel op te slaan.\n", - "* `AND`/`OR` zijn klassen om AND/OR-takken van de boom te vertegenwoordigen. Ze slaan gewoon de lijst van argumenten op. Om de code te vereenvoudigen, is alle functionaliteit gedefinieerd in de bovenliggende klasse `Content`.\n" + "Laten we proberen een eenvoudige taal te definiëren voor kennisrepresentatie gebaseerd op productieregels. We zullen Python-klassen gebruiken als sleutelwoorden om regels te definiëren. Er zijn in wezen 3 soorten klassen:\n", + "* `Ask` vertegenwoordigt een vraag die aan de gebruiker gesteld moet worden. Het bevat de set mogelijke antwoorden.\n", + "* `If` vertegenwoordigt een regel, en het is slechts syntactische suiker om de inhoud van de regel op te slaan\n", + "* `AND`/`OR` zijn klassen om AND/OR-takken van de boom te vertegenwoordigen. Ze slaan gewoon de lijst met argumenten binnenin op. Om de code te vereenvoudigen, is alle functionaliteit gedefinieerd in de bovenliggende klasse `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In ons systeem zou het werkgeheugen de lijst van **feiten** bevatten als **attribuut-waardeparen**. De kennisbasis kan worden gedefinieerd als één grote woordenlijst die acties (nieuwe feiten die in het werkgeheugen moeten worden ingevoegd) koppelt aan voorwaarden, uitgedrukt als EN-OF-expressies. Ook kunnen sommige feiten worden `Gevraagd`.\n" + "In ons systeem zou het werkgeheugen de lijst van **feiten** bevatten als **attribuut-waardeparen**. De kennisbasis kan worden gedefinieerd als één groot woordenboek dat acties (nieuwe feiten die in het werkgeheugen moeten worden ingevoerd) afbeeldt op voorwaarden, uitgedrukt als AND-OR expressies. Ook kunnen sommige feiten worden `gevraagd` (`Ask`).\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Om de achterwaartse inferentie uit te voeren, definiëren we de klasse `Knowledgebase`. Deze zal bevatten:\n", - "* Werkgeheugen (`memory`) - een woordenboek dat attributen aan waarden koppelt\n", - "* Kennisbankregels (`rules`) in het hierboven gedefinieerde formaat\n", + "Om de backward inference uit te voeren, definiëren we de klasse `Knowledgebase`. Deze zal bevatten:\n", + "* Werkend `memory` - een woordenboek dat attributen aan waarden koppelt\n", + "* Knowledgebase `rules` in het hierboven gedefinieerde formaat\n", "\n", - "Twee belangrijkste methoden zijn:\n", - "* `get` om de waarde van een attribuut te verkrijgen, waarbij indien nodig inferentie wordt uitgevoerd. Bijvoorbeeld, `get('color')` haalt de waarde van een kleur-slot op (het zal indien nodig vragen stellen en de waarde opslaan voor later gebruik in het werkgeheugen). Als we `get('color:blue')` vragen, zal het om een kleur vragen en vervolgens een `y`/`n`-waarde retourneren afhankelijk van de kleur.\n", - "* `eval` voert de daadwerkelijke inferentie uit, d.w.z. doorloopt de AND/OR-boom, evalueert subdoelen, enzovoort.\n" + "Twee hoofdmethoden zijn:\n", + "* `get` om de waarde van een attribuut te verkrijgen, waarbij indien nodig inferentie wordt uitgevoerd. Bijvoorbeeld, `get('color')` zou de waarde van een kleur-slot ophalen (het zal vragen indien nodig, en de waarde opslaan voor later gebruik in het werkgeheugen). Als we `get('color:blue')` vragen, zal het naar een kleur vragen en vervolgens een `y`/`n` waarde retourneren afhankelijk van de kleur.\n", + "* `eval` voert de eigenlijke inferentie uit, dat wil zeggen, doorloopt AND/OR bomen, evalueert subdoelen, enz.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Laten we nu onze dierenkennisbank definiëren en de consultatie uitvoeren. Merk op dat deze oproep u vragen zal stellen. U kunt antwoorden door `y`/`n` te typen voor ja-nee vragen, of door een nummer (0..N) op te geven voor vragen met langere meerkeuzeantwoorden.\n" + "Laten we nu onze dierenkennisbasis definiëren en het consult uitvoeren. Let op dat deze oproep je vragen zal stellen. Je kunt antwoorden door `y`/`n` te typen voor ja-nee vragen, of door een nummer (0..N) op te geven voor vragen met langere meerkeuzeantwoorden.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow gebruiken voor Forward Inference\n", + "## Gebruik van Experta voor Voorwaartse Inferentie\n", "\n", - "In het volgende voorbeeld gaan we proberen forward inference te implementeren met behulp van een van de bibliotheken voor kennisrepresentatie, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** is een bibliotheek voor het creëren van forward inference-systemen in Python, die is ontworpen om vergelijkbaar te zijn met het klassieke oude systeem [CLIPS](http://www.clipsrules.net/index.html).\n", + "In het volgende voorbeeld zullen we proberen voorwaartse inferentie te implementeren met behulp van een van de bibliotheken voor kennisrepresentatie, [Experta](https://github.com/nilp0inter/experta). **Experta** is een bibliotheek voor het maken van voorwaartse inferentiesystemen in Python, die is ontworpen om vergelijkbaar te zijn met het klassieke oude systeem [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "We hadden forward chaining ook zelf kunnen implementeren zonder al te veel problemen, maar eenvoudige implementaties zijn meestal niet erg efficiënt. Voor effectievere regelmatching wordt een speciaal algoritme, [Rete](https://en.wikipedia.org/wiki/Rete_algorithm), gebruikt.\n" + "We hadden ook zelf voorwaartse chaining kunnen implementeren zonder veel problemen, maar naïeve implementaties zijn meestal niet erg efficiënt. Voor effectievere regelmatching wordt een speciaal algoritme [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) gebruikt.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We zullen ons systeem definiëren als een klasse die `KnowledgeEngine` subclass. Elke regel wordt gedefinieerd door een aparte functie met de annotatie `@Rule`, die specificeert wanneer de regel moet worden uitgevoerd. Binnen de regel kunnen we nieuwe feiten toevoegen met de functie `declare`, en het toevoegen van die feiten zal ertoe leiden dat meer regels worden aangeroepen door de forward inference engine.\n" + "We definiëren ons systeem als een klasse die `KnowledgeEngine` als superklasse heeft. Elke regel wordt gedefinieerd door een aparte functie met de `@Rule`-annotatie, die specificeert wanneer de regel moet worden geactiveerd. Binnen de regel kunnen we nieuwe feiten toevoegen met de functie `declare`, en het toevoegen van die feiten zal ertoe leiden dat er meer regels worden aangeroepen door de voorwaartse inferentiemotor.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Zodra we een kennisbasis hebben gedefinieerd, vullen we ons werkgeheugen met enkele initiële feiten en roepen vervolgens de methode `run()` aan om de inferentie uit te voeren. Je kunt als resultaat zien dat nieuwe afgeleide feiten aan het werkgeheugen worden toegevoegd, inclusief het uiteindelijke feit over het dier (als we alle initiële feiten correct hebben ingesteld).\n" + "Zodra we een kennisbasis hebben gedefinieerd, vullen we ons werkgeheugen met enkele initiële feiten en roepen we vervolgens de methode `run()` aan om de inferentie uit te voeren. Je kunt zien dat als resultaat nieuwe afgeleide feiten worden toegevoegd aan het werkgeheugen, inclusief het uiteindelijke feit over het dier (als we alle initiële feiten correct hebben ingesteld).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Disclaimer**: \nDit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, dient u zich ervan bewust te zijn dat geautomatiseerde vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal moet worden beschouwd als de gezaghebbende bron. Voor kritieke informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling.\n" + "---\n\n\n**Disclaimer**: \nDit document is vertaald met behulp van de AI-vertalingsdienst [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, kan deze automatische vertaling fouten of onnauwkeurigheden bevatten. Het originele document in de oorspronkelijke taal moet als gezaghebbende bron worden beschouwd. Voor cruciale informatie wordt een professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T21:05:14+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T02:52:22+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "nl" } diff --git a/translations/nl/lessons/2-Symbolic/README.md b/translations/nl/lessons/2-Symbolic/README.md index eee102f4..7aea7022 100644 --- a/translations/nl/lessons/2-Symbolic/README.md +++ b/translations/nl/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Kennisrepresentatie en Expertsystemen +# Kennisrepresentatie en Expert Systemen -![Samenvatting van Symbolische AI-inhoud](../../../../translated_images/nl/ai-symbolic.715a30cb610411a6.webp) +![Samenvatting van Symbolische AI-inhoud](../../../../../../translated_images/nl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote door [Tomomi Imura](https://twitter.com/girlie_mac) -De zoektocht naar kunstmatige intelligentie is gebaseerd op het zoeken naar kennis, om de wereld te begrijpen zoals mensen dat doen. Maar hoe kun je dit aanpakken? +De zoektocht naar kunstmatige intelligentie is gebaseerd op een zoektocht naar kennis, om de wereld te begrijpen op een manier die lijkt op hoe mensen dat doen. Maar hoe kun je dit aanpakken? -## [Quiz voorafgaand aan de les](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/3) -In de vroege dagen van AI was de top-down benadering om intelligente systemen te creëren (besproken in de vorige les) populair. Het idee was om kennis van mensen te extraheren in een machineleesbare vorm en deze vervolgens te gebruiken om automatisch problemen op te lossen. Deze aanpak was gebaseerd op twee grote ideeën: +In de vroege dagen van AI was de top-down benadering om intelligente systemen te creëren (besproken in de vorige les) populair. Het idee was om kennis van mensen te extraheren in een machine-leesbare vorm, en deze vervolgens te gebruiken om automatisch problemen op te lossen. Deze benadering was gebaseerd op twee grote ideeën: * Kennisrepresentatie * Redeneren ## Kennisrepresentatie -Een van de belangrijke concepten in Symbolische AI is **kennis**. Het is belangrijk om kennis te onderscheiden van *informatie* of *data*. Bijvoorbeeld, men kan zeggen dat boeken kennis bevatten, omdat je boeken kunt bestuderen en een expert kunt worden. Wat boeken echter bevatten, wordt eigenlijk *data* genoemd, en door boeken te lezen en deze data te integreren in ons wereldmodel, zetten we deze data om in kennis. +Een van de belangrijke concepten in Symbolische AI is **kennis**. Het is belangrijk om kennis te onderscheiden van *informatie* of *data*. Bijvoorbeeld, men kan zeggen dat boeken kennis bevatten, omdat men boeken kan bestuderen en een expert kan worden. Echter, wat boeken bevatten wordt eigenlijk *data* genoemd, en door boeken te lezen en deze data te integreren in ons wereldmodel zetten we deze data om in kennis. -> ✅ **Kennis** is iets dat in ons hoofd zit en onze begrip van de wereld vertegenwoordigt. Het wordt verkregen door een actief **leerproces**, waarbij stukjes informatie die we ontvangen worden geïntegreerd in ons actieve wereldmodel. +> ✅ **Kennis** is iets dat in ons hoofd zit en onze begrip van de wereld vertegenwoordigt. Het wordt verkregen door een actief **leerproces**, dat stukjes informatie die we ontvangen integreert in ons actieve wereldmodel. -Meestal definiëren we kennis niet strikt, maar brengen we het in lijn met andere gerelateerde concepten via de [DIKW-piramide](https://en.wikipedia.org/wiki/DIKW_pyramid). Deze bevat de volgende concepten: +Meestal definiëren we kennis niet strikt, maar stemmen het af met andere gerelateerde concepten met behulp van de [DIKW-piramide](https://en.wikipedia.org/wiki/DIKW_pyramid). Deze bevat de volgende concepten: -* **Data** is iets dat wordt weergegeven op fysieke media, zoals geschreven tekst of gesproken woorden. Data bestaat onafhankelijk van mensen en kan tussen mensen worden doorgegeven. +* **Data** is iets dat wordt weergegeven in fysiek medium, zoals geschreven tekst of gesproken woorden. Data bestaat onafhankelijk van mensen en kan tussen mensen worden doorgegeven. * **Informatie** is hoe we data interpreteren in ons hoofd. Bijvoorbeeld, wanneer we het woord *computer* horen, hebben we een bepaald begrip van wat het is. -* **Kennis** is informatie die wordt geïntegreerd in ons wereldmodel. Bijvoorbeeld, zodra we leren wat een computer is, beginnen we ideeën te krijgen over hoe het werkt, hoeveel het kost en waarvoor het kan worden gebruikt. Dit netwerk van onderling verbonden concepten vormt onze kennis. -* **Wijsheid** is nog een niveau van ons begrip van de wereld en vertegenwoordigt *meta-kennis*, bijvoorbeeld een idee over hoe en wanneer de kennis moet worden gebruikt. +* **Kennis** is informatie die geïntegreerd is in ons wereldmodel. Bijvoorbeeld, zodra we leren wat een computer is, beginnen we ideeën te krijgen over hoe het werkt, hoeveel het kost, en waar het voor kan worden gebruikt. Dit netwerk van onderling verbonden concepten vormt onze kennis. +* **Wijsheid** is nog een niveau dieper in ons begrip van de wereld, en vertegenwoordigt *meta-kennis*, bijvoorbeeld een notie over hoe en wanneer de kennis moet worden gebruikt. - + -*Afbeelding [van Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Door Longlivetheux - Eigen werk, CC BY-SA 4.0* +*Afbeelding [van Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), door Longlivetheux - Eigen werk, CC BY-SA 4.0* -Het probleem van **kennisrepresentatie** is dus om een effectieve manier te vinden om kennis binnen een computer in de vorm van data te representeren, zodat het automatisch bruikbaar is. Dit kan worden gezien als een spectrum: +Dus, het probleem van **kennisrepresentatie** is het vinden van een effectieve manier om kennis binnen een computer te representeren in de vorm van data, om het automatisch bruikbaar te maken. Dit kan worden gezien als een spectrum: -![Spectrum van kennisrepresentatie](../../../../translated_images/nl/knowledge-spectrum.b60df631852c0217.webp) +![Spectrum van kennisrepresentatie](../../../../../../translated_images/nl/knowledge-spectrum.b60df631852c0217.webp) > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) -* Aan de linkerkant zijn er zeer eenvoudige soorten kennisrepresentaties die effectief door computers kunnen worden gebruikt. De eenvoudigste is algoritmisch, waarbij kennis wordt weergegeven door een computerprogramma. Dit is echter niet de beste manier om kennis te representeren, omdat het niet flexibel is. Kennis in ons hoofd is vaak niet-algoritmisch. -* Aan de rechterkant zijn er representaties zoals natuurlijke tekst. Dit is het krachtigste, maar kan niet worden gebruikt voor automatisch redeneren. +* Links zijn zeer eenvoudige typen kennisrepresentaties die effectief door computers kunnen worden gebruikt. De eenvoudigste is algoritmisch, waarbij kennis wordt weergegeven door een computerprogramma. Dit is echter niet de beste manier om kennis te representeren, omdat het niet flexibel is. Kennis in ons hoofd is vaak niet-algoritmisch. +* Rechts zijn representaties zoals natuurlijke tekst. Dit is het krachtigst, maar kan niet voor automatisch redeneren worden gebruikt. -> ✅ Denk een minuut na over hoe je kennis in je hoofd representeert en omzet in notities. Is er een specifiek formaat dat goed voor je werkt om het te onthouden? +> ✅ Denk even na over hoe jij kennis in je hoofd representeert en omzet in aantekeningen. Is er een bepaald formaat dat goed voor jou werkt om het beter te onthouden? -## Classificatie van computerkennisrepresentaties +## Indelen van Computer Kennisrepresentation -We kunnen verschillende methoden voor computerkennisrepresentatie classificeren in de volgende categorieën: +We kunnen verschillende methoden voor computer-kennisrepresentatie indelen in de volgende categorieën: -* **Netwerkrepresentaties** zijn gebaseerd op het feit dat we een netwerk van onderling verbonden concepten in ons hoofd hebben. We kunnen proberen dezelfde netwerken als een grafiek binnen een computer te reproduceren - een zogenaamde **semantisch netwerk**. +* **Netwerkrepresentaties** zijn gebaseerd op het feit dat we een netwerk van onderling verbonden concepten in ons hoofd hebben. We kunnen proberen dezezelfde netwerken als een graaf in een computer weer te geven - een zogenaamde **semantisch netwerk**. -1. **Object-Attribuut-Waarde triplets** of **attribuut-waarde paren**. Omdat een grafiek binnen een computer kan worden weergegeven als een lijst van knooppunten en randen, kunnen we een semantisch netwerk representeren door een lijst van triplets, met objecten, attributen en waarden. Bijvoorbeeld, we bouwen de volgende triplets over programmeertalen: +1. **Object-Attribute-Value triplets** of **attribuut-waarde paren**. Aangezien een graaf in een computer kan worden weergegeven als een lijst van knopen en verbindingen, kunnen we een semantisch netwerk representeren door een lijst van triplets, die objecten, attributen en waarden bevatten. Bijvoorbeeld, we bouwen de volgende triplets over programmeertalen: Object | Attribuut | Waarde --------|-----------|------ +-------|-----------|------- Python | is | Untyped-Language -Python | uitgevonden-door | Guido van Rossum -Python | blok-syntaxis | inspringing -Untyped-Language | heeft geen | type-definities +Python | invented-by | Guido van Rossum +Python | block-syntax | indentation +Untyped-Language | doesn't have | type definitions > ✅ Denk na over hoe triplets kunnen worden gebruikt om andere soorten kennis te representeren. -2. **Hiërarchische representaties** benadrukken het feit dat we vaak een hiërarchie van objecten in ons hoofd creëren. Bijvoorbeeld, we weten dat een kanarie een vogel is, en alle vogels hebben vleugels. We hebben ook een idee over welke kleur een kanarie meestal heeft en wat hun vliegsnelheid is. +2. **Hiërarchische representaties** benadrukken het feit dat we vaak een hiërarchie van objecten in ons hoofd creëren. Bijvoorbeeld, we weten dat een kanarie een vogel is, en dat alle vogels vleugels hebben. We hebben ook een idee welke kleur een kanarie meestal heeft, en wat hun vliegsnelheid is. - - **Frame-representatie** is gebaseerd op het representeren van elk object of klasse van objecten als een **frame** dat **slots** bevat. Slots hebben mogelijke standaardwaarden, waardebeperkingen of opgeslagen procedures die kunnen worden opgeroepen om de waarde van een slot te verkrijgen. Alle frames vormen een hiërarchie vergelijkbaar met een objecthiërarchie in objectgeoriënteerde programmeertalen. - - **Scenario's** zijn een speciaal soort frames die complexe situaties representeren die zich in de tijd kunnen ontvouwen. + - **Frame-representatie** is gebaseerd op het representeren van elk object of klasse van objecten als een **frame** dat **slots** bevat. Slots hebben mogelijke standaardwaarden, waardebeperkingen of opgeslagen procedures die kunnen worden aangeroepen om de waarde van een slot te verkrijgen. Alle frames vormen een hiërarchie vergelijkbaar met een objecthiërarchie in objectgeoriënteerde programmeertalen. + - **Scenario's** zijn een speciaal soort frames die complexe situaties vertegenwoordigen die zich in de tijd kunnen ontvouwen. **Python** Slot | Waarde | Standaardwaarde | Interval | ------|-------|---------------|----------| -Naam | Python | | | +-----|--------|-----------------|----------| +Name | Python | | | Is-A | Untyped-Language | | | -Variabele Notatie | | CamelCase | | -Programmalengte | | | 5-5000 regels | -Blok-syntaxis | Inspringing | | | +Variable Case | | CamelCase | | +Program Length | | | 5-5000 regels | +Block Syntax | Indent | | | 3. **Procedurele representaties** zijn gebaseerd op het representeren van kennis door een lijst van acties die kunnen worden uitgevoerd wanneer een bepaalde conditie optreedt. - - Productieregels zijn if-then statements waarmee we conclusies kunnen trekken. Bijvoorbeeld, een arts kan een regel hebben die zegt dat **ALS** een patiënt hoge koorts heeft **OF** een hoog niveau van C-reactief proteïne in een bloedtest **DAN** hij een ontsteking heeft. Zodra we een van de condities tegenkomen, kunnen we een conclusie trekken over ontsteking en deze vervolgens gebruiken in verdere redenering. - - Algoritmen kunnen worden beschouwd als een andere vorm van procedurele representatie, hoewel ze bijna nooit direct worden gebruikt in kennisgebaseerde systemen. + - Productieregels zijn als-dan uitspraken die ons toestaan conclusies te trekken. Bijvoorbeeld, een dokter kan een regel hebben die zegt dat **ALS** een patiënt hoge koorts heeft **OF** een hoog niveau van C-reactief proteïne in bloedtest **DAN** heeft hij een ontsteking. Zodra we aan een van de condities voldoen, kunnen we een conclusie trekken over de ontsteking, en deze vervolgens gebruiken in verder redeneren. + - Algoritmes kunnen worden beschouwd als een andere vorm van procedurele representatie, hoewel ze bijna nooit direct worden gebruikt in kennisgebaseerde systemen. 4. **Logica** werd oorspronkelijk voorgesteld door Aristoteles als een manier om universele menselijke kennis te representeren. - - Predicatenlogica als een wiskundige theorie is te rijk om berekenbaar te zijn, daarom wordt normaal gesproken een subset ervan gebruikt, zoals Horn-clausules die worden gebruikt in Prolog. - - Beschrijvende logica is een familie van logische systemen die worden gebruikt om hiërarchieën van objecten en gedistribueerde kennisrepresentaties zoals *semantisch web* te representeren en te redeneren. + - Predicaatlogica als wiskundige theorie is te rijk om berekenbaar te zijn, daarom wordt normaal een deelverzameling ervan gebruikt, zoals Horn-clausules gebruikt in Prolog. + - Beschrijvende Logica is een familie van logische systemen die worden gebruikt om te representeren en redeneren over hiërarchieën van objecten en gedistribueerde kennisrepresentaties zoals *semantisch web*. -## Expertsystemen +## Expert Systemen -Een van de vroege successen van symbolische AI waren de zogenaamde **expertsystemen** - computersystemen die waren ontworpen om als expert te functioneren in een beperkt probleemgebied. Ze waren gebaseerd op een **kennisbasis** die was geëxtraheerd van een of meer menselijke experts, en ze bevatten een **inferentie-engine** die enige redenering uitvoerde bovenop deze basis. +Een van de vroege successen van symbolische AI waren zogenaamde **expert systemen** - computersystemen die ontworpen waren om als een expert te functioneren in een beperkt probleemdomein. Ze waren gebaseerd op een **kennisbasis** die werd geëxtraheerd van een of meerdere menselijke experts, en ze bevatten een **inference engine** die er bovenop redeneerde. -![Menselijke Architectuur](../../../../translated_images/nl/arch-human.5d4d35f1bba3ab1c.webp) | ![Kennisgebaseerd Systeem](../../../../translated_images/nl/arch-kbs.3ec5c150b09fa8da.webp) +![Menselijke Architectuur](../../../../../../translated_images/nl/arch-human.5d4d35f1bba3ab1c.webp) | ![Kennisgebaseerd Systeem](../../../../../../translated_images/nl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Vereenvoudigde structuur van een menselijk neuraal systeem | Architectuur van een kennisgebaseerd systeem +Vereenvoudigde structuur van een menselijk neuronensysteem | Architectuur van een kennisgebaseerd systeem -Expertsystemen zijn gebouwd zoals het menselijke redeneersysteem, dat **kortetermijngeheugen** en **langetermijngeheugen** bevat. Evenzo onderscheiden we in kennisgebaseerde systemen de volgende componenten: +Expert systemen zijn opgebouwd als het menselijke redeneringssysteem, dat een **korte termijn geheugen** en een **lange termijn geheugen** bevat. Op dezelfde manier onderscheiden we in kennisgebaseerde systemen de volgende componenten: -* **Probleemgeheugen**: bevat de kennis over het probleem dat momenteel wordt opgelost, bijvoorbeeld de temperatuur of bloeddruk van een patiënt, of hij een ontsteking heeft of niet, enz. Deze kennis wordt ook wel **statische kennis** genoemd, omdat het een momentopname bevat van wat we momenteel weten over het probleem - de zogenaamde *probleemstatus*. -* **Kennisbasis**: vertegenwoordigt langetermijnkennis over een probleemgebied. Het wordt handmatig geëxtraheerd van menselijke experts en verandert niet van consultatie tot consultatie. Omdat het ons in staat stelt te navigeren van de ene probleemstatus naar de andere, wordt het ook wel **dynamische kennis** genoemd. -* **Inferentie-engine**: orkestreert het hele proces van zoeken in de probleemstatusruimte, stelt vragen aan de gebruiker wanneer nodig. Het is ook verantwoordelijk voor het vinden van de juiste regels die op elke status moeten worden toegepast. +* **Probleemgeheugen**: bevat kennis over het probleem dat op dat moment wordt opgelost, bijvoorbeeld de temperatuur of bloeddruk van een patiënt, of hij een ontsteking heeft of niet, enz. Deze kennis wordt ook wel **statische kennis** genoemd, omdat het een momentopname bevat van wat we momenteel weten over het probleem - de zogenaamde *probleemtoestand*. +* **Kennisbasis**: vertegenwoordigt lange termijn kennis over een probleemdomein. Deze is handmatig geëxtraheerd van menselijke experts, en verandert niet van consult tot consult. Omdat het ons toestaat te navigeren van de ene probleemtoestand naar de andere, wordt het ook **dynamische kennis** genoemd. +* **Inference engine**: orkestreert het hele proces van zoeken in de probleemtoestandruimte, stelt vragen aan de gebruiker waar nodig. Het is ook verantwoordelijk om de juiste regels te vinden die op elke toestand moeten worden toegepast. -Als voorbeeld bekijken we het volgende expertsysteem om een dier te bepalen op basis van zijn fysieke kenmerken: +Als voorbeeld nemen we het volgende expertsysteem om een dier te bepalen op basis van zijn fysieke kenmerken: -![AND-OR Boom](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR Boom](../../../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.webp) > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) -Dit diagram wordt een **AND-OR boom** genoemd en is een grafische representatie van een set productieregels. Het tekenen van een boom is nuttig aan het begin van het extraheren van kennis van de expert. Om de kennis binnen de computer te representeren, is het handiger om regels te gebruiken: +Dit diagram wordt een **AND-OR boom** genoemd, en het is een grafische representatie van een set productieregels. Het tekenen van een boom is nuttig aan het begin van het extraheren van kennis van de expert. Om de kennis binnen de computer te representeren is het handiger regels te gebruiken: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Je kunt zien dat elke conditie aan de linkerkant van de regel en de actie in wezen object-attribuut-waarde (OAV) triplets zijn. **Werkgeheugen** bevat de set OAV-triplets die overeenkomen met het probleem dat momenteel wordt opgelost. Een **regels-engine** zoekt naar regels waarvan een conditie is voldaan en past deze toe, waarbij een nieuwe triplet aan het werkgeheugen wordt toegevoegd. +Je kunt opmerken dat elke conditie aan de linkerkant van de regel en de actie in essentie object-attribute-value (OAV) triplets zijn. **Werkgeheugen** bevat de set OAV-triplets die overeenkomen met het probleem dat op dat moment wordt opgelost. Een **regels-engine** zoekt naar regels waarvan een conditie is voldaan en past ze toe, door een nieuwe triplet aan het werkgeheugen toe te voegen. -> ✅ Maak je eigen AND-OR boom over een onderwerp dat je leuk vindt! +> ✅ Schrijf je eigen AND-OR boom over een onderwerp dat je leuk vindt! -### Voorwaartse vs. Achterwaartse Inferentie +### Voorwaartse versus Achterwaartse Redenering -Het hierboven beschreven proces wordt **voorwaartse inferentie** genoemd. Het begint met enkele initiële gegevens over het probleem die beschikbaar zijn in het werkgeheugen en voert vervolgens de volgende redeneercyclus uit: +Het hierboven beschreven proces wordt **voorwaartse redenering** genoemd. Het begint met initiële gegevens over het probleem dat beschikbaar is in het werkgeheugen, en voert vervolgens de volgende redeneerlus uit: 1. Als het doelattribuut aanwezig is in het werkgeheugen - stop en geef het resultaat -2. Zoek naar alle regels waarvan de conditie momenteel is voldaan - verkrijg **conflictset** van regels. -3. Voer **conflictresolutie** uit - selecteer één regel die in deze stap zal worden uitgevoerd. Er kunnen verschillende conflictresolutiestrategieën zijn: - - Selecteer de eerste toepasbare regel in de kennisbasis +2. Zoek alle regels waarvan de conditie momenteel is voldaan - verkrijg **conflictset** van regels. +3. Voer **conflictresolutie** uit - selecteer één regel die in deze stap wordt uitgevoerd. Er kunnen verschillende conflictresolutiestrategieën zijn: + - Selecteer de eerste toepasselijke regel in de kennisbasis - Selecteer een willekeurige regel - - Selecteer een *meer specifieke* regel, d.w.z. degene die aan de meeste condities aan de "linkerkant" (LHS) voldoet -4. Pas de geselecteerde regel toe en voeg een nieuw stukje kennis toe aan de probleemstatus + - Selecteer een *meer specifieke* regel, d.w.z. degene die aan de meeste voorwaarden aan de "linkerkant" (LHS) voldoet +4. Pas de geselecteerde regel toe en voeg een nieuw stuk kennis toe aan de probleemtoestand 5. Herhaal vanaf stap 1. -In sommige gevallen willen we echter beginnen met een lege kennis over het probleem en vragen stellen die ons helpen tot een conclusie te komen. Bijvoorbeeld, bij het stellen van een medische diagnose voeren we meestal niet alle medische analyses vooraf uit voordat we beginnen met het diagnosticeren van de patiënt. We willen eerder analyses uitvoeren wanneer een beslissing moet worden genomen. +In sommige gevallen willen we echter misschien beginnen met een lege kennis over het probleem, en vragen stellen die ons helpen tot een conclusie te komen. Bijvoorbeeld, bij het stellen van een medische diagnose voeren we gewoonlijk niet alle medische analyses uit voordat we met de diagnose beginnen. We willen liever analyses uitvoeren wanneer er een beslissing genomen moet worden. -Dit proces kan worden gemodelleerd met **achterwaartse inferentie**. Het wordt gedreven door het **doel** - de attribuutwaarde die we proberen te vinden: +Dit proces kan worden gemodelleerd met **achterwaartse redenering**. Het wordt gestuurd door het **doel** - de attribuutwaarde die we proberen te vinden: -1. Selecteer alle regels die ons de waarde van een doel kunnen geven (d.w.z. met het doel aan de RHS ("rechterkant")) - een conflictset -1. Als er geen regels zijn voor dit attribuut, of er is een regel die zegt dat we de waarde van de gebruiker moeten vragen - vraag erom, anders: -1. Gebruik een conflictresolutiestrategie om één regel te selecteren die we als *hypothese* zullen gebruiken - we zullen proberen deze te bewijzen -1. Herhaal het proces recursief voor alle attributen in de LHS van de regel, waarbij we proberen ze als doelen te bewijzen +1. Selecteer alle regels die ons de waarde van een doel kunnen geven (d.w.z. met het doel aan de rechterkant ("right-hand-side")) - een conflictset +1. Als er geen regels voor dit attribuut zijn, of als er een regel is die zegt dat we de waarde aan de gebruiker moeten vragen - vraag deze dan, anders: +1. Gebruik conflictresolutiestrategie om een regel te selecteren die we als *hypothese* zullen gebruiken - we proberen deze te bewijzen +1. Herhaal herhaaldelijk het proces voor alle attributen in de LHS van de regel, waarbij we proberen deze te bewijzen als doelen 1. Als het proces op enig moment faalt - gebruik een andere regel bij stap 3. -> ✅ In welke situaties is voorwaartse inferentie meer geschikt? En achterwaartse inferentie? +> ✅ In welke situaties is voorwaartse redenering geschikter? Hoe zit het met achterwaartse redenering? -### Implementatie van Expertsystemen +### Implementeren van Expert Systemen -Expertsystemen kunnen worden geïmplementeerd met verschillende tools: +Expertsystemen kunnen worden geïmplementeerd met verschillende hulpmiddelen: -* Ze direct programmeren in een hoog niveau programmeertaal. Dit is niet de beste optie, omdat het belangrijkste voordeel van een kennisgebaseerd systeem is dat kennis gescheiden is van inferentie, en een probleemdomeinexpert mogelijk regels moet kunnen schrijven zonder de details van het inferentieproces te begrijpen. -* Gebruik maken van een **expertsystemen-shell**, d.w.z. een systeem dat specifiek is ontworpen om te worden gevuld met kennis met behulp van een kennisrepresentatietaal. +* Direct programmeren in een hoge-niveau programmeertaal. Dit is niet de beste aanpak, omdat het grootste voordeel van een kennisgebaseerd systeem is dat kennis is gescheiden van inference, en potentieel een deskundige in het probleemgebied zelf regels zou moeten kunnen schrijven zonder de details van het inferentieproces te begrijpen +* Het gebruik van een **expert systems shell**, dat wil zeggen, een systeem dat specifiek is ontworpen om gevuld te worden met kennis met behulp van een bepaalde kennisrepresentatietaal. -## ✍️ Oefening: Dierlijke Inferentie +## ✍️ Oefening: Dier-Redenering -Zie [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) voor een voorbeeld van het implementeren van een voorwaartse en achterwaartse inferentie-expertsysteem. +Zie [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) voor een voorbeeld van het implementeren van een voorwaarts- en achterwaarts redenerend expertsysteem. -> **Opmerking**: Dit voorbeeld is vrij eenvoudig en geeft alleen een idee van hoe een expertsysteem eruitziet. Zodra je begint met het maken van zo'n systeem, zul je pas enige *intelligente* gedrag ervan opmerken zodra je een bepaald aantal regels bereikt, rond de 200+. Op een gegeven moment worden regels te complex om ze allemaal in gedachten te houden, en op dat moment kun je je gaan afvragen waarom een systeem bepaalde beslissingen neemt. Een belangrijk kenmerk van kennisgebaseerde systemen is echter dat je altijd precies kunt *uitleggen* hoe een van de beslissingen is genomen. +> **Opmerking**: Dit voorbeeld is vrij eenvoudig en geeft alleen een idee van hoe een expertsysteem eruit ziet. Zodra je begint met het creëren van zo'n systeem, zal je alleen een *intelligent* gedrag ervan opmerken zodra je een bepaald aantal regels bereikt, rond de 200+. Op een gegeven moment worden regels te complex om allemaal in gedachten te houden, en op dat punt vraag je je misschien af waarom een systeem bepaalde beslissingen neemt. Echter, het belangrijke kenmerk van kennisgebaseerde systemen is dat je altijd kunt *uitleggen* hoe elke beslissing precies werd genomen. ## Ontologieën en het Semantisch Web -Aan het einde van de 20e eeuw was er een initiatief om kennisrepresentatie te gebruiken om internetbronnen te annoteren, zodat het mogelijk zou zijn om bronnen te vinden die overeenkomen met zeer specifieke zoekopdrachten. Deze beweging werd **Semantisch Web** genoemd en was gebaseerd op verschillende concepten: +Aan het einde van de 20e eeuw was er een initiatief om kennisrepresentatie te gebruiken om internetbronnen te annoteren, zodat het mogelijk zou zijn bronnen te vinden die aan zeer specifieke zoekopdrachten voldoen. Deze beweging heette **Semantisch Web**, en was gebaseerd op verschillende concepten: -- Een speciale kennisrepresentatie gebaseerd op **[beschrijvende logica](https://en.wikipedia.org/wiki/Description_logic)** (DL). Het lijkt op frame-kennisrepresentatie, omdat het een hiërarchie van objecten met eigenschappen opbouwt, maar het heeft formele logische semantiek en inferentie. Er is een hele familie van DL's die een balans vinden tussen expressiviteit en algoritmische complexiteit van inferentie. -- Gedistribueerde kennisrepresentatie, waarbij alle concepten worden vertegenwoordigd door een globale URI-identificator, waardoor het mogelijk is om kennishiërarchieën te creëren die het internet overspannen. -- Een familie van XML-gebaseerde talen voor kennisbeschrijving: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- Een speciale kennisrepresentatie gebaseerd op **[description logics](https://en.wikipedia.org/wiki/Description_logic)** (DL). Het lijkt op frame-kennisrepresentatie, omdat het een hiërarchie van objecten met eigenschappen opbouwt, maar het heeft formele logische semantiek en inferentie. Er is een hele familie van DL's die een balans vinden tussen expressiviteit en algoritmische complexiteit van inferentie. +- Gedistribueerde kennisrepresentatie, waarbij alle concepten worden vertegenwoordigd door een globale URI-identificator, wat het mogelijk maakt kennis-hiërarchieën te creëren die het internet overspannen. +- Een familie van op XML gebaseerde talen voor kennisbeschrijving: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Een kernconcept in het Semantisch Web is het concept van **Ontologie**. Dit verwijst naar een expliciete specificatie van een probleemdomein met behulp van een formele kennisrepresentatie. De eenvoudigste ontologie kan gewoon een hiërarchie van objecten in een probleemdomein zijn, maar complexere ontologieën bevatten regels die gebruikt kunnen worden voor inferentie. +Een kernconcept in het Semantische Web is het concept van **Ontologie**. Dit verwijst naar een expliciete specificatie van een probleemgebied met behulp van een formele kennisrepresentatie. De eenvoudigste ontologie kan gewoon een hiërarchie van objecten in een probleemgebied zijn, maar complexere ontologieën bevatten regels die gebruikt kunnen worden voor afleiding. -In het semantisch web zijn alle representaties gebaseerd op triplets. Elk object en elke relatie wordt uniek geïdentificeerd door een URI. Bijvoorbeeld, als we willen aangeven dat dit AI Curriculum is ontwikkeld door Dmitry Soshnikov op 1 januari 2022, dan kunnen we de volgende triplets gebruiken: +In het semantische web zijn alle representaties gebaseerd op triplets. Elk object en elke relatie worden uniek geïdentificeerd door de URI. Bijvoorbeeld, als we het feit willen aangeven dat deze AI Curriculum is ontwikkeld door Dmitry Soshnikov op 1 januari 2022 - hier zijn de triplets die we kunnen gebruiken: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Hier zijn `http://www.example.com/terms/creation-date` en `http://purl.org/dc/elements/1.1/creator` enkele bekende en universeel geaccepteerde URI's om de concepten *maker* en *aanmaakdatum* uit te drukken. +> ✅ Hier zijn `http://www.example.com/terms/creation-date` en `http://purl.org/dc/elements/1.1/creator` enkele bekende en universeel geaccepteerde URI’s om de concepten van *maker* en *creatiedatum* uit te drukken. -In een complexer geval, als we een lijst van makers willen definiëren, kunnen we enkele datastructuren gebruiken die in RDF zijn gedefinieerd. +In een complexer geval, als we een lijst van makers willen definiëren, kunnen we enkele gegevensstructuren gebruiken die in RDF zijn gedefinieerd. - + > Diagrammen hierboven door [Dmitry Soshnikov](http://soshnikov.com) -De voortgang van het bouwen van het Semantisch Web werd enigszins vertraagd door het succes van zoekmachines en technieken voor natuurlijke taalverwerking, die gestructureerde gegevens uit tekst kunnen halen. Toch zijn er in sommige gebieden nog steeds aanzienlijke inspanningen om ontologieën en kennisbanken te onderhouden. Enkele noemenswaardige projecten: +De vooruitgang in het bouwen van het Semantische Web werd enigszins afgeremd door het succes van zoekmachines en technieken voor natuurlijke taalverwerking, waarmee gestructureerde data uit tekst kan worden gehaald. Echter, in sommige gebieden zijn er nog steeds aanzienlijke inspanningen om ontologieën en kennisbanken te onderhouden. Enkele opmerkelijke projecten: -* [WikiData](https://wikidata.org/) is een verzameling machineleesbare kennisbanken die gekoppeld zijn aan Wikipedia. De meeste gegevens worden gehaald uit Wikipedia *InfoBoxes*, stukjes gestructureerde inhoud binnen Wikipedia-pagina's. Je kunt [WikiData bevragen](https://query.wikidata.org/) in SPARQL, een speciale querytaal voor het Semantisch Web. Hier is een voorbeeldquery die de meest populaire oogkleuren onder mensen weergeeft: +* [WikiData](https://wikidata.org/) is een verzameling van machine-leesbare kennisbanken gekoppeld aan Wikipedia. Het merendeel van de data wordt gewonnen uit Wikipedia *InfoBoxes*, stukken gestructureerde inhoud binnen Wikipedia-pagina’s. Je kunt [query’s uitvoeren](https://query.wikidata.org/) op wikidata in SPARQL, een speciale querytaal voor het Semantische Web. Hier is een voorbeeldquery die de meest populaire oogkleuren onder mensen toont: ```sparql #defaultView:BubbleChart @@ -208,45 +208,50 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) is een andere inspanning vergelijkbaar met WikiData. -> ✅ Als je wilt experimenteren met het bouwen van je eigen ontologieën, of bestaande wilt openen, is er een geweldige visuele ontologie-editor genaamd [Protégé](https://protege.stanford.edu/). Download het, of gebruik het online. +> ✅ Als je wilt experimenteren met het bouwen van je eigen ontologieën, of bestaande openen, is er een geweldige visuele ontologie-editor genaamd [Protégé](https://protege.stanford.edu/). Download het of gebruik het online. - + *Web Protégé-editor geopend met de Romanov Familie-ontologie. Screenshot door Dmitry Soshnikov* -## ✍️ Oefening: Een Familie-Ontologie +## ✍️ Oefening: Een Familie Ontologie -Zie [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) voor een voorbeeld van het gebruik van technieken uit het Semantisch Web om te redeneren over familiebanden. We nemen een stamboom in het gebruikelijke GEDCOM-formaat en een ontologie van familiebanden en bouwen een grafiek van alle familiebanden voor een gegeven set individuen. + +Bekijk [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) voor een voorbeeld van het gebruik van Semantische Web-technieken om redeneerwerk over familiebanden uit te voeren. We nemen een stamboom die is weergegeven in het veelgebruikte GEDCOM-formaat en een ontologie van familiebanden en bouwen een graaf van alle familiebanden voor een gegeven set individuen. ## Microsoft Concept Graph -In de meeste gevallen worden ontologieën zorgvuldig met de hand gemaakt. Het is echter ook mogelijk om ontologieën te **ontginnen** uit ongestructureerde gegevens, bijvoorbeeld uit teksten in natuurlijke taal. +In de meeste gevallen worden ontologieën zorgvuldig met de hand gemaakt. Het is echter ook mogelijk om ontologieën te **delven** uit ongestructureerde data, bijvoorbeeld uit natuurlijke taalteksten. -Een dergelijke poging werd gedaan door Microsoft Research en resulteerde in de [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Een dergelijke poging werd gedaan door Microsoft Research, en resulteerde in de [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Dit is een grote verzameling entiteiten die gegroepeerd zijn met behulp van de `is-a` overervingsrelatie. Het stelt ons in staat om vragen te beantwoorden zoals "Wat is Microsoft?" - het antwoord zou iets kunnen zijn als "een bedrijf met waarschijnlijkheid 0,87, en een merk met waarschijnlijkheid 0,75". +Dit is een grote verzameling entiteiten, gegroepeerd met behulp van de `is-a` erfelijkheidsrelatie. Het maakt het mogelijk vragen zoals "Wat is Microsoft?" te beantwoorden - het antwoord is dan iets van "een bedrijf met een waarschijnlijkheid van 0.87, en een merk met een waarschijnlijkheid van 0.75". -De grafiek is beschikbaar als REST API of als een groot downloadbaar tekstbestand dat alle entiteitparen opsomt. +De grafiek is beschikbaar als REST API, of als een grote downloadbare tekstbestand die alle entiteitenparen opsomt. ## ✍️ Oefening: Een Conceptgrafiek -Probeer de [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notebook om te zien hoe we Microsoft Concept Graph kunnen gebruiken om nieuwsartikelen in verschillende categorieën te groeperen. +Probeer het [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notitieboek om te zien hoe we de Microsoft Concept Graph kunnen gebruiken om nieuwsartikelen in verschillende categorieën te groeperen. ## Conclusie -Tegenwoordig wordt AI vaak gezien als een synoniem voor *Machine Learning* of *Neurale Netwerken*. Een mens vertoont echter ook expliciet redeneren, iets wat momenteel niet wordt afgehandeld door neurale netwerken. In echte projecten wordt expliciet redeneren nog steeds gebruikt om taken uit te voeren die uitleg vereisen, of om het gedrag van het systeem op een gecontroleerde manier aan te passen. +Tegenwoordig wordt AI vaak beschouwd als een synoniem voor *Machine Learning* of *Neurale Netwerken*. Echter, een mens vertoont ook expliciet redeneren, iets dat momenteel niet door neurale netwerken wordt behandeld. In projecten in de echte wereld wordt expliciet redeneren nog steeds gebruikt voor taken die uitleg vereisen, of waarmee het gedrag van het systeem op een gecontroleerde manier kan worden aangepast. ## 🚀 Uitdaging -In de Family Ontology-notebook die bij deze les hoort, is er een mogelijkheid om te experimenteren met andere familiebanden. Probeer nieuwe verbindingen tussen mensen in de stamboom te ontdekken. +In het Family Ontology-notitieboek dat bij deze les hoort, is er een mogelijkheid om te experimenteren met andere familiebanden. Probeer nieuwe connecties tussen mensen in de stamboom te ontdekken. ## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## Review & Zelfstudie -Doe wat onderzoek op internet om gebieden te ontdekken waar mensen hebben geprobeerd kennis te kwantificeren en te codificeren. Bekijk Bloom's Taxonomy en ga terug in de geschiedenis om te leren hoe mensen probeerden hun wereld te begrijpen. Verken het werk van Linnaeus om een taxonomie van organismen te creëren en observeer hoe Dmitri Mendelejev een manier ontwikkelde om chemische elementen te beschrijven en te groeperen. Welke andere interessante voorbeelden kun je vinden? +Doe wat onderzoek op het internet om gebieden te ontdekken waar mensen hebben geprobeerd kennis te kwantificeren en te codificeren. Kijk naar de taxonomie van Bloom en ga terug in de geschiedenis om te leren hoe mensen probeerden hun wereld te begrijpen. Verken het werk van Linnaeus om een taxonomie van organismen te creëren, en observeer de manier waarop Dmitri Mendeleev een methode creëerde om chemische elementen te beschrijven en te groeperen. Welke andere interessante voorbeelden kun je vinden? **Opdracht**: [Bouw een Ontologie](assignment.md) --- + +**Disclaimer**: +Dit document is vertaald met behulp van de AI-vertalingsdienst [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, dient u er rekening mee te houden dat automatische vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal wordt beschouwd als de gezaghebbende bron. Voor cruciale informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor enige misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling. + \ No newline at end of file diff --git a/translations/no/README.md b/translations/no/README.md index b4c7b71c..16a12cdd 100644 --- a/translations/no/README.md +++ b/translations/no/README.md @@ -1,8 +1,8 @@ -[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Kinesisk (forenklet)](../zh/README.md) | [Kinesisk (tradisjonell, Hong Kong)](../hk/README.md) | [Kinesisk (tradisjonell, Macau)](../mo/README.md) | [Kinesisk (tradisjonell, Taiwan)](../tw/README.md) | [Kroatisk](../hr/README.md) | [Tsjekkisk](../cs/README.md) | [Dansk](../da/README.md) | [Nederlandsk](../nl/README.md) | [Estisk](../et/README.md) | [Finsk](../fi/README.md) | [Fransk](../fr/README.md) | [Tysk](../de/README.md) | [Gresk](../el/README.md) | [Hebraisk](../he/README.md) | [Hindi](../hi/README.md) | [Ungarsk](../hu/README.md) | [Indonesisk](../id/README.md) | [Italiensk](../it/README.md) | [Japansk](../ja/README.md) | [Kannada](../kn/README.md) | [Koreansk](../ko/README.md) | [Litauisk](../lt/README.md) | [Malayisk](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalesisk](../ne/README.md) | [Nigeriansk pidgin](../pcm/README.md) | [Norsk](./README.md) | [Persisk (Farsi)](../fa/README.md) | [Polsk](../pl/README.md) | [Portugisisk (Brasil)](../br/README.md) | [Portugisisk (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumensk](../ro/README.md) | [Russisk](../ru/README.md) | [Serbisk (kyrillisk)](../sr/README.md) | [Slovakisk](../sk/README.md) | [Slovensk](../sl/README.md) | [Spansk](../es/README.md) | [Swahili](../sw/README.md) | [Svensk](../sv/README.md) | [Tagalog (Filippinsk)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Tyrkisk](../tr/README.md) | [Ukrainsk](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamesisk](../vi/README.md) +[Arabisk](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarsk](../bg/README.md) | [Burmesisk (Myanmar)](../my/README.md) | [Kinesisk (Forenklet)](../zh/README.md) | [Kinesisk (Tradisjonell, Hong Kong)](../hk/README.md) | [Kinesisk (Tradisjonell, Macao)](../mo/README.md) | [Kinesisk (Tradisjonell, Taiwan)](../tw/README.md) | [Kroatisk](../hr/README.md) | [Tsjekkisk](../cs/README.md) | [Dansk](../da/README.md) | [Nederlandsk](../nl/README.md) | [Estisk](../et/README.md) | [Finsk](../fi/README.md) | [Fransk](../fr/README.md) | [Tysk](../de/README.md) | [Gresk](../el/README.md) | [Hebraisk](../he/README.md) | [Hindi](../hi/README.md) | [Ungarsk](../hu/README.md) | [Indonesisk](../id/README.md) | [Italiensk](../it/README.md) | [Japansk](../ja/README.md) | [Kannada](../kn/README.md) | [Koreansk](../ko/README.md) | [Litauisk](../lt/README.md) | [Malaysisk](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalesisk](../ne/README.md) | [Nigeriansk Pidgin](../pcm/README.md) | [Norsk](./README.md) | [Persisk (Farsi)](../fa/README.md) | [Polsk](../pl/README.md) | [Portugisisk (Brasil)](../br/README.md) | [Portugisisk (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumensk](../ro/README.md) | [Russisk](../ru/README.md) | [Serbisk (Kyrillisk)](../sr/README.md) | [Slovakisk](../sk/README.md) | [Slovensk](../sl/README.md) | [Spansk](../es/README.md) | [Swahili](../sw/README.md) | [Svensk](../sv/README.md) | [Tagalog (Filippinsk)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Tyrkisk](../tr/README.md) | [Ukrainsk](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamesisk](../vi/README.md) > **Foretrekker du å klone lokalt?** -> Dette depotet inkluderer 50+ språkoversettelser som betydelig øker nedlastningsstørrelsen. For å klone uten oversettelser, bruk sparse checkout: +> Dette depotet inkluderer 50+ språkoversettelser som betydelig øker nedlastingsstørrelsen. For å klone uten oversettelser, bruk sparsommelig utvalg: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners @@ -48,7 +47,7 @@ Utforsk verden av **Kunstig Intelligens** (AI) med vårt 12-ukers, 24-leksjoners > Dette gir deg alt du trenger for å fullføre kurset med en mye raskere nedlasting. -**Hvis du ønsker at flere oversettelsesspråk skal støttes er de oppført [her](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Dersom du ønsker ytterligere støttede oversettelsesspråk, er disse listet [her](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Bli med i fellesskapet [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -57,123 +56,123 @@ Utforsk verden av **Kunstig Intelligens** (AI) med vårt 12-ukers, 24-leksjoners **[Tankekart over kurset](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -I dette undervisningsopplegget vil du lære: +I dette pensumet vil du lære: -* Ulike tilnærminger til kunstig intelligens, inkludert den "gode gamle" symbolske tilnærmingen med **Kunnskapsrepresentasjon** og resonnement ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Nevrale nettverk** og **Dyp læring**, som er kjernen i moderne AI. Vi vil illustrere konseptene bak disse viktige temaene ved bruk av kode i to av de mest populære rammeverkene - [TensorFlow](http://Tensorflow.org) og [PyTorch](http://pytorch.org). -* **Neurale arkitekturer** for arbeid med bilder og tekst. Vi vil dekke nyere modeller, men kan være noe mangelfull på det mest avanserte. -* Mindre populære AI-tilnærminger, som **Genetiske algoritmer** og **Multi-Agent Systemer**. +* Ulike tilnærminger til kunstig intelligens, inkludert den "gode gamle" symbolske tilnærmingen med **kunnskapsrepresentasjon** og resonnement ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Neurale nettverk** og **dyp læring**, som er kjernen i moderne KI. Vi vil illustrere konseptene bak disse viktige temaene ved bruk av kode i to av de mest populære rammeverkene - [TensorFlow](http://Tensorflow.org) og [PyTorch](http://pytorch.org). +* **Neurale arkitekturer** for arbeid med bilder og tekst. Vi vil dekke nyere modeller, men kan mangle noe av det siste innen forskning. +* Mindre populære KI-tilnærminger, slik som **genetiske algoritmer** og **multi-agent systemer**. -Hva vi ikke vil dekke i dette undervisningsopplegget: +Hva vi ikke vil dekke i dette pensumet: > [Finn alle tilleggsmaterialer for dette kurset i vår Microsoft Learn-samling](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Forretningssaker for bruk av **AI i næringslivet**. Vurder å ta [Introduksjon til AI for forretningsbrukere](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) på Microsoft Learn, eller [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), utviklet i samarbeid med [INSEAD](https://www.insead.edu/). -* **Klassisk maskinlæring**, som er godt beskrevet i vårt [Maskinlæring for nybegynnere undervisningsopplegg](http://github.com/Microsoft/ML-for-Beginners). -* Praktiske AI-applikasjoner bygget med **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. For dette anbefaler vi at du starter med moduler på Microsoft Learn for [syn](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [naturlig språkbehandling](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generativ AI med Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** og andre. -* Spesifikke ML **sky-rammeverk**, som [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), eller [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Vurder å bruke [Bygg og drift maskinlæringsløsninger med Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) og [Bygg og drift maskinlæringsløsninger med Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) læringsstier. -* **Samtale-AI** og **Chat Bots**. Det finnes en egen [Lag samtale-AI-løsninger](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) læringssti, og du kan også se [denne bloggposten](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for mer detalj. +* Forretningsscenarier for bruk av **KI i næringslivet**. Vurder å ta [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum)-læringssti på Microsoft Learn, eller [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), utviklet i samarbeid med [INSEAD](https://www.insead.edu/). +* **Klassisk maskinlæring**, som er godt beskrevet i vårt [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). +* Praktiske KI-applikasjoner bygget med **[Kognitive tjenester](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. For dette anbefaler vi at du starter med Microsoft Learn-moduler for [syn](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [naturlig språkbehandling](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generativ KI med Azure OpenAI-tjeneste](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** og andre. +* Spesifikke ML **Cloud-rammeverk**, som [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), eller [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Vurder å bruke læringsstiene [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) og [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Samtale-KI** og **Chat-boter**. Det finnes en egen [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) læringssti, og du kan også se [denne bloggposten](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for mer detaljert informasjon. * **Dyp matematikk** bak dyp læring. For dette anbefaler vi [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) av Ian Goodfellow, Yoshua Bengio og Aaron Courville, som også er tilgjengelig online på [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -For en mild introduksjon til _AI i skyen_ temaer kan du vurdere å ta [Kom i gang med kunstig intelligens på Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) læringssti. +For en enkel introduksjon til _KI i skyen_-temaer kan du vurdere å ta [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) Learning Path. # Innhold -| | Leksjonslenke | PyTorch/Keras/TensorFlow | Lab | +| | Leksjonslenke | PyTorch/Keras/TensorFlow | Laboratorium | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Kursoppsett](./lessons/0-course-setup/setup.md) | [Sett opp utviklingsmiljøet ditt](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Introduksjon til AI**](./lessons/1-Intro/README.md) | | | -| 01 | [Introduksjon og historikk for AI](./lessons/1-Intro/README.md) | - | - | -| II | **Symbolsk AI** | +| 0 | [Kursoppsett](./lessons/0-course-setup/setup.md) | [Sett opp ditt utviklingsmiljø](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Introduksjon til KI**](./lessons/1-Intro/README.md) | | | +| 01 | [Introduksjon og historie om KI](./lessons/1-Intro/README.md) | - | - | +| II | **Symbolsk KI** | | 02 | [Kunnskapsrepresentasjon og ekspertsystemer](./lessons/2-Symbolic/README.md) | [Ekspertsystemer](./lessons/2-Symbolic/Animals.ipynb) / [Ontologi](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Konseptgraf](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introduksjon til nevrale nettverk**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Flerlags Perceptron og Lage vårt eget Rammeverk](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Introduksjon til Rammeverk (PyTorch/TensorFlow) og Overtilpasning](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| 04 | [Multi-Layered Perceptron og Lage vårt eget rammeverk](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Introduksjon til rammeverk (PyTorch/TensorFlow) og overtilpasning](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**Computer Vision**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Utforsk Computer Vision på Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Introduksjon til Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Konvolusjonelle Nevrale Nettverk](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Arkitekturer](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Forhåndstrente Nettverk og Overføringslæring](./lessons/4-ComputerVision/08-TransferLearning/README.md) og [Treningstriks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoenkodere og VAEer](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generative Adversarial Networks & Artistisk Stilovertakelse](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 07 | [Konvolusjonale nevrale nettverk](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN-arkitekturer](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Forhåndstrente nettverk og overføringslæring](./lessons/4-ComputerVision/08-TransferLearning/README.md) og [Treningstriks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoenkodere og VAE-arkitektur](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Generative adversariell nettverk og kunstnerisk stiloverføring](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Objektdeteksjon](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [Semantisk Segmentering. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Naturlig Språkbehandling**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Utforsk Naturlig Språkbehandling på Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 12 | [Semantisk segmentering. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**Naturlig språkbehandling**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Utforsk naturlig språkbehandling på Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Tekstreprensentasjon. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Semantiske ordinnpakninger. Word2Vec og GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Språkmodellering. Trene dine egne innpakninger](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Rekurrente Nevrale Nettverk](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Generative Rekurrente Nettverk](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 14 | [Semantiske ordinnbeddings. Word2Vec og GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Språkmodellering. Trene dine egne innbeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Rekurrente nevrale nettverk](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [Generative rekurrente nettverk](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Navngitt Entitetsgjenkjenning](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Store Språkmodeller, Prompt-programmering og Få-skudds-oppgaver](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Andre AI Teknikker** || | -| 21 | [Genetiske Algoritmer](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Dyp Forsterkende Læring](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Multi-agent Systemer](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI Etikk** | | | -| 24 | [AI Etikk og Ansvarlig AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Ansvarlige AI Prinsipper](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 19 | [Navngitt enhetsgjenkjenning](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Store språkmodeller, promptprogrammering og få-skudd-oppgaver](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Andre AI-teknikker** || | +| 21 | [Genetiske algoritmer](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Dyp forsterkningslæring](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Multi-agent systemer](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI-etikk** | | | +| 24 | [AI-etikk og ansvarlig AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Ansvarlige AI-prinsipper](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Ekstra** | | | -| 25 | [Multimodale Nettverk, CLIP og VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Multimodale nettverk, CLIP og VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Hver leksjon inneholder -* Forhåndslesningsmateriale -* Kjørbare Jupyter Notebooks, som ofte er spesifikke for rammeverket (**PyTorch** eller **TensorFlow**). Den kjørbare notebooken inneholder også mye teoretisk materiale, så for å forstå emnet må du gå gjennom minst én versjon av notebooken (enten PyTorch eller TensorFlow). -* **Labber** tilgjengelig for noen emner, som gir deg muligheten til å prøve å bruke materialet du har lært på et spesifikt problem. -* Noen seksjoner inneholder lenker til [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) moduler som dekker relaterte emner. +* Forhåndslesingsmateriale +* Utførbare Jupyter Notebooks, som ofte er spesifikke for rammeverket (**PyTorch** eller **TensorFlow**). Den utførbare notatboken inneholder også mye teoretisk materiale, så for å forstå emnet må du gå igjennom minst én versjon av notatboken (enten PyTorch eller TensorFlow). +* **Labber** tilgjengelig for noen temaer, som gir deg en mulighet til å prøve å anvende materialet du har lært på et spesifikt problem. +* Noen seksjoner inneholder lenker til [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) moduler som dekker relaterte temaer. ## Komme i gang -### 🎯 Ny i AI? Start her! +### 🎯 Ny til AI? Start her! -Hvis du er helt ny i AI og vil ha raske, praktiske eksempler, sjekk ut våre [**Nybegynnervennlige eksempler**](./examples/README.md)! Disse inkluderer: +Hvis du er helt ny til AI og ønsker raske, praktiske eksempler, sjekk ut våre [**Begynnervennlige eksempler**](./examples/README.md)! Disse inkluderer: - 🌟 **Hello AI World** - Ditt første AI-program (mønster-gjenkjenning) -- 🧠 **Enkelt Nevralt Nettverk** - Bygg et nevralt nettverk fra bunnen av +- 🧠 **Enkelt nevralt nettverk** - Bygg et nevralt nettverk fra bunnen av - 🖼️ **Bildklassifiserer** - Klassifiser bilder med detaljerte kommentarer - 💬 **Tekststemning** - Analyser positiv/negativ tekst -Disse eksemplene er laget for å hjelpe deg å forstå AI-konsepter før du går videre i hele pensumet. +Disse eksemplene er laget for å hjelpe deg å forstå AI-konsepter før du dykker inn i hele læreplanen. -### 📚 Full pensumoppsett +### 📚 Full Læreplanoppsett -- Vi har laget en [oppsett-leksjon](./lessons/0-course-setup/setup.md) for å hjelpe deg med å sette opp utviklingsmiljøet ditt. - For lærere har vi også laget en [pensumoppsett-leksjon](./lessons/0-course-setup/for-teachers.md)! -- Hvordan [kjøre koden i VSCode eller Codepace](./lessons/0-course-setup/how-to-run.md) +- Vi har laget en [oppsett-leksjon](./lessons/0-course-setup/setup.md) for å hjelpe deg med å sette opp utviklingsmiljøet ditt. - For lærere har vi også laget en [læreplansoppsett-leksjon](./lessons/0-course-setup/for-teachers.md)! +- Hvordan [kjøre koden i VSCode eller en Codespace](./lessons/0-course-setup/how-to-run.md) Følg disse trinnene: -Fork depotet: Klikk på "Fork" knappen øverst til høyre på denne siden. +Fork repositoryet: Klikk på "Fork" knappen øverst til høyre på denne siden. -Klon depotet: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Klon repositoryet: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Ikke glem å gi stjerne (🌟) til dette repoet for lettere å finne det senere. +Ikke glem å stjerne (🌟) dette repoet for å finne det lettere senere. ## Møt andre lærende -Bli med i vår [offisielle AI Discord-server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) for å møte og nettverke med andre som tar dette kurset og få støtte. +Bli med på vår [offisielle AI Discord-server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) for å møte og nettverke med andre som tar dette kurset og få støtte. -Hvis du har produktinnspill eller spørsmål mens du bygger, besøk vår [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Hvis du har produktfeedback eller spørsmål mens du bygger, besøk vår [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) -## Quizzer +## Quizzer -> **En merknad om quizzer**: Alle quizzer finnes i Quiz-app-mappen i etc\quiz-app, eller [Online her](https://ff-quizzes.netlify.app/) De er linket fra leksjonene, quiz-appen kan kjøres lokalt eller distribueres til Azure; følg instruksjonen i `quiz-app` mappen. De blir gradvis oversatt. +> **En merknad om quizzer**: Alle quizzer ligger i Quiz-app mappen i etc\quiz-app, eller [Online her](https://ff-quizzes.netlify.app/) De er lenket fra leksjonene, quiz-appen kan kjøres lokalt eller distribueres til Azure; følg instruksjonene i `quiz-app` mappen. De lokaliseres gradvis. ## Hjelp ønskes -Har du forslag eller funnet stavefeil eller kodefeil? Opprett en issue eller en pull request. +Har du forslag eller funnet stave- eller kodefeil? Opprett en issue eller pull request. ## Spesiell takk * **✍️ Hovedforfatter:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Redaktør:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Sketchnote illustratør:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Quizskaper:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 Sketchnote-illustratør:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Quiz-skaper:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Kjernebidragsytere:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Andre pensum +## Andre læreplaner -Teamet vårt produserer andre pensum! Sjekk ut: +Teamet vårt lager andre læreplaner! Sjekk ut: ### LangChain @@ -189,15 +188,15 @@ Teamet vårt produserer andre pensum! Sjekk ut: [![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### Generativ AI-serie + +### Generativ AI Serie [![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) [![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - + ### Kjerneopplæring [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) @@ -208,8 +207,8 @@ Teamet vårt produserer andre pensum! Sjekk ut: [![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### Copilot-serie + +### Copilot Serie [![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) @@ -217,11 +216,11 @@ Teamet vårt produserer andre pensum! Sjekk ut: ## Få hjelp -Hvis du sitter fast eller har spørsmål om å bygge AI-apper, bli med andre lærende og erfarne utviklere i diskusjoner om MCP. Det er et støttende fellesskap hvor spørsmål er velkomne og kunnskap deles fritt. +Hvis du står fast eller har spørsmål om å bygge AI-apper. Bli med andre lærende og erfarne utviklere i diskusjoner om MCP. Det er et støttende fellesskap hvor spørsmål er velkomne og kunnskap deles fritt. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Hvis du har produktinnspill eller finner feil mens du bygger, besøk: +Hvis du har produktfeedback eller feil mens du bygger, besøk: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -229,5 +228,5 @@ Hvis du har produktinnspill eller finner feil mens du bygger, besøk: **Ansvarsfraskrivelse**: -Dette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi streber etter nøyaktighet, vennligst vær oppmerksom på at automatiske oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på sitt opprinnelige språk skal betraktes som den autoritative kilden. For viktig informasjon anbefales profesjonell menneskelig oversettelse. Vi påtar oss ikke ansvar for eventuelle misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen. +Dette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi streber etter nøyaktighet, vennligst vær oppmerksom på at automatiske oversettelser kan inneholde feil eller unøyaktigheter. Det opprinnelige dokumentet på dets opprinnelige språk bør anses som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for eventuelle misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen. \ No newline at end of file diff --git a/translations/no/lessons/0-course-setup/how-to-run.md b/translations/no/lessons/0-course-setup/how-to-run.md index 3025184a..faf854d6 100644 --- a/translations/no/lessons/0-course-setup/how-to-run.md +++ b/translations/no/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Hvordan kjøre koden -Dette kurset inneholder mange eksekverbare eksempler og laboratorier som du vil ønske å kjøre. For å gjøre dette trenger du muligheten til å kjøre Python-kode i Jupyter Notebooks som er inkludert i dette kurset. Du har flere alternativer for å kjøre koden: +Dette pensumet inneholder mange kjørbare eksempler og laboratorier som du vil kjøre. For å gjøre dette trenger du muligheten til å kjøre Python-kode i Jupyter Notebooks som følger med i dette pensumet. Du har flere alternativer for å kjøre koden: -## Kjøre lokalt på din datamaskin +## Kjør lokalt på din egen datamaskin -For å kjøre koden lokalt på din datamaskin, må du ha en versjon av Python installert. Jeg anbefaler personlig å installere **[miniconda](https://conda.io/en/latest/miniconda.html)** - det er en lettvektsinstallasjon som støtter `conda` pakkebehandler for ulike Python **virtuelle miljøer**. +For å kjøre koden lokalt på din datamaskin, trengs en Python-installasjon. En anbefaling er å installere **[miniconda](https://conda.io/en/latest/miniconda.html)** - det er en ganske lett installasjon som støtter `conda` pakkebehandler for forskjellige Python **virtuelle miljøer**. -Etter at du har installert miniconda, må du klone repositoryen og opprette et virtuelt miljø som skal brukes for dette kurset: +Etter at du har installert miniconda, klon depotet og opprett et virtuelt miljø som skal brukes for dette kurset: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Bruke Visual Studio Code med Python-utvidelse +### Bruke Visual Studio Code med Python Extension -Den beste måten å bruke kurset på er sannsynligvis å åpne det i [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) med [Python-utvidelse](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Dette pensumet fungerer best når du åpner det i [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) med [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Når du kloner og åpner katalogen i VS Code, vil det automatisk foreslå at du installerer Python-utvidelser. Du må også installere miniconda som beskrevet ovenfor. +> **Merk**: Når du har klonet og åpnet mappen i VS Code, vil det automatisk foreslå deg å installere Python-utvidelser. Du må også installere miniconda som beskrevet ovenfor. -> **Note**: Hvis VS Code foreslår at du åpner repositoryen i en container, må du avslå dette for å bruke lokal Python-installasjon. +> **Merk**: Hvis VS Code foreslår deg å åpne depotet på nytt i en container, bør du avslå dette for å bruke den lokale Python-installasjonen. ### Bruke Jupyter i nettleseren -Du kan også bruke Jupyter-miljøet direkte fra nettleseren på din egen datamaskin. Faktisk gir både klassisk Jupyter og Jupyter Hub et ganske praktisk utviklingsmiljø med autoutfylling, kodefremheving, osv. +Du kan også bruke et Jupyter-miljø fra nettleseren på din egen datamaskin. Både klassisk Jupyter og JupyterHub tilbyr et praktisk utviklingsmiljø med automatisk utfylling, kodeutheving, osv. -For å starte Jupyter lokalt, gå til katalogen for kurset og kjør: +For å starte Jupyter lokalt, gå til mappen for kurset, og kjør: ```bash jupyter notebook @@ -45,34 +45,36 @@ eller ```bash jupyterhub ``` -Du kan deretter navigere til en av `.ipynb`-filene, åpne dem og begynne å jobbe. +Du kan da navigere til hvilke som helst `.ipynb`-filer, åpne dem og begynne å jobbe. -### Kjøre i container +### Kjøring i container -Et alternativ til Python-installasjon er å kjøre koden i en container. Siden repositoryen vår inneholder en spesiell `.devcontainer`-mappe som instruerer hvordan man bygger en container for dette repoet, vil VS Code tilby deg å åpne koden i en container. Dette krever Docker-installasjon og er mer komplekst, så vi anbefaler dette for mer erfarne brukere. +Et alternativ til Python-installasjon er å kjøre koden i en container. Siden vårt depot inneholder en spesiell `.devcontainer`-mappe som beskriver hvordan man bygger en container for dette depotet, tilbyr VS Code muligheten til å åpne koden i en container. Dette krever Docker-installasjon, og vil også være mer komplekst, så vi anbefaler dette for mer erfarne brukere. -## Kjøre i skyen +## Kjøring i skyen -Hvis du ikke ønsker å installere Python lokalt og har tilgang til noen skyressurser, er et godt alternativ å kjøre koden i skyen. Det finnes flere måter du kan gjøre dette på: +Hvis du ikke ønsker å installere Python lokalt, og har tilgang til noen skyressurser – vil det være et godt alternativ å kjøre koden i skyen. Det finnes flere måter du kan gjøre dette på: -* Bruke **[GitHub Codespaces](https://github.com/features/codespaces)**, som er et virtuelt miljø opprettet for deg på GitHub, tilgjengelig gjennom VS Code-nettlesergrensesnittet. Hvis du har tilgang til Codespaces, kan du bare klikke på **Code**-knappen i repoet, starte en codespace og komme i gang på kort tid. -* Bruke **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) er gratis databehandlingsressurser tilgjengelig i skyen for folk som deg til å teste ut kode på GitHub. Det er en knapp på forsiden for å åpne repositoryen i Binder - dette bør raskt ta deg til Binder-siden, som vil bygge den underliggende containeren og starte Jupyter-nettgrensesnittet for deg sømløst. +* Bruke **[GitHub Codespaces](https://github.com/features/codespaces)**, som er et virtuelt miljø opprettet for deg på GitHub, tilgjengelig gjennom en VS Code nettlesergrensesnitt. Hvis du har tilgang til Codespaces, kan du bare trykke på **Code**-knappen i depotet, starte et codespace, og komme i gang på kort tid. +* Bruke **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) tilbyr gratis databehandlingsressurser i skyen for folk som deg for å teste ut kode på GitHub. Det finnes en knapp på forsiden for å åpne depotet i Binder – dette vil raskt ta deg til binder-siden, som vil bygge en underliggende container og starte et Jupyter-nettgrensesnitt for deg sømløst. -> **Note**: For å forhindre misbruk har Binder tilgang til noen nettressurser blokkert. Dette kan forhindre at noe av koden fungerer, som henter modeller og/eller datasett fra offentlig Internett. Du må kanskje finne noen løsninger. Dessuten er databehandlingsressursene som tilbys av Binder ganske grunnleggende, så trening vil være treg, spesielt i senere mer komplekse leksjoner. +> **Merk**: For å forhindre misbruk har Binder blokkert tilgang til noen nettressurser. Dette kan forhindre at noe av koden fungerer, som henter modeller og/eller datasett fra det offentlige Internett. Du må kanskje finne noen alternative løsninger. Også de beregningsressursene som tilbys av Binder er ganske enkle, så trening vil være treg, spesielt i senere, mer komplekse leksjoner. -## Kjøre i skyen med GPU +## Kjøring i skyen med GPU -Noen av de senere leksjonene i dette kurset vil ha stor nytte av GPU-støtte, fordi trening ellers vil være smertefullt tregt. Det finnes noen alternativer du kan følge, spesielt hvis du har tilgang til skyen enten gjennom [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) eller gjennom din institusjon: +Noen av de senere leksjonene i dette pensumet vil ha stor nytte av GPU-støtte. Modelltrening kan ellers bli smertefullt langsom. Det finnes noen få alternativer du kan følge, spesielt hvis du har tilgang til skyen enten via [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), eller gjennom din utdanningsinstitusjon: -* Opprett [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) og koble til den via Jupyter. Du kan deretter klone repoet direkte på maskinen og begynne å lære. NC-serien VMs har GPU-støtte. +* Opprett [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) og koble til den via Jupyter. Du kan da klone depotet rett på maskinen, og begynne å lære. NC-serie VM-er har GPU-støtte. -> **Note**: Noen abonnementer, inkludert Azure for Students, gir ikke GPU-støtte som standard. Du må kanskje be om ekstra GPU-kjerner gjennom en teknisk supportforespørsel. +> **Merk**: Noen abonnementer, inkludert Azure for Students, tilbyr ikke GPU-støtte som standard. Du må kanskje be om ekstra GPU-kjerner via en teknisk støtteforespørsel. -* Opprett [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) og bruk deretter Notebook-funksjonen der. [Denne videoen](https://azure-for-academics.github.io/quickstart/azureml-papers/) viser hvordan du kloner et repository inn i Azure ML-notebook og begynner å bruke det. +* Opprett [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) og bruk deretter Notebook-funksjonen der. [Denne videoen](https://azure-for-academics.github.io/quickstart/azureml-papers/) viser hvordan du kloner et depot inn i Azure ML-notatbok og begynner å bruke det. Du kan også bruke Google Colab, som kommer med noe gratis GPU-støtte, og laste opp Jupyter Notebooks der for å kjøre dem én etter én. --- + **Ansvarsfraskrivelse**: -Dette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi streber etter nøyaktighet, vær oppmerksom på at automatiserte oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på sitt opprinnelige språk bør anses som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen. \ No newline at end of file +Dette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi søker å oppnå nøyaktighet, vennligst vær oppmerksom på at automatiske oversettelser kan inneholde feil eller unøyaktigheter. Det opprinnelige dokumentet på originalspråket bør anses som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for misforståelser eller feiltolkninger som følge av bruk av denne oversettelsen. + \ No newline at end of file diff --git a/translations/no/lessons/2-Symbolic/Animals.ipynb b/translations/no/lessons/2-Symbolic/Animals.ipynb index 5f4f209e..473caafc 100644 --- a/translations/no/lessons/2-Symbolic/Animals.ipynb +++ b/translations/no/lessons/2-Symbolic/Animals.ipynb @@ -10,21 +10,21 @@ "\n", "Et eksempel fra [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "I dette eksempelet skal vi implementere et enkelt kunnskapsbasert system for å identifisere et dyr basert på noen fysiske egenskaper. Systemet kan representeres av følgende AND-OR-tre (dette er en del av hele treet, vi kan enkelt legge til flere regler):\n", + "I dette eksemplet skal vi implementere et enkelt kunnskapsbasert system for å bestemme et dyr basert på noen fysiske kjennetegn. Systemet kan representeres ved følgende AND-OR-tre (dette er en del av hele treet, vi kan enkelt legge til flere regler):\n", "\n", - "![](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Vår egen ekspertsystemskall med bakoverinferens\n", + "## Vårt eget ekspertsystemskall med bakoverinferenz\n", "\n", - "La oss prøve å definere et enkelt språk for kunnskapsrepresentasjon basert på produksjonsregler. Vi vil bruke Python-klasser som nøkkelord for å definere regler. Det vil i hovedsak være tre typer klasser:\n", - "* `Ask` representerer et spørsmål som må stilles til brukeren. Den inneholder settet med mulige svar.\n", - "* `If` representerer en regel, og det er bare syntaktisk sukker for å lagre innholdet i regelen.\n", - "* `AND`/`OR` er klasser som representerer AND/OR-grener i treet. De lagrer bare listen over argumenter inni. For å forenkle koden er all funksjonalitet definert i foreldresklassen `Content`.\n" + "La oss prøve å definere et enkelt språk for kunnskapsrepresentasjon basert på produksjonsregler. Vi vil bruke Python-klasser som nøkkelord for å definere regler. Det vil i hovedsak være 3 typer klasser:\n", + "* `Ask` representerer et spørsmål som må stilles til brukeren. Den inneholder settet av mulige svar.\n", + "* `If` representerer en regel, og det er bare syntaktisk sukker for å lagre innholdet i regelen\n", + "* `AND`/`OR` er klasser for å representere AND/OR-grener i treet. De lagrer bare listen av argumenter inne. For å forenkle koden, er all funksjonalitet definert i overklasse `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "I vårt system vil arbeidsminnet inneholde listen over **fakta** som **attributt-verdi-par**. Kunnskapsbasen kan defineres som en stor ordbok som kobler handlinger (nye fakta som skal settes inn i arbeidsminnet) til betingelser, uttrykt som OG-ELLER-uttrykk. I tillegg kan noen fakta bli `Spurt`.\n" + "I vårt system ville arbeidsminnet inneholde listen over **fakta** som **attributt-verdi-par**. Kunnskapsbasen kan defineres som en stor ordbok som kobler handlinger (nye fakta som skal settes inn i arbeidsminnet) til betingelser, uttrykt som OG-ELLER-uttrykk. Også noen fakta kan `Ask`es.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For å utføre baklengs slutning, vil vi definere klassen `Knowledgebase`. Den vil inneholde:\n", - "* Arbeidsminne (`memory`) - en ordbok som kobler attributter til verdier\n", - "* Kunnskapsbase (`rules`) i formatet som definert ovenfor\n", + "For å utføre bakoverinference vil vi definere klassen `Knowledgebase`. Den vil inneholde:\n", + "* Arbeidende `memory` - et oppslagsord som kobler attributter til verdier\n", + "* Kunnskapsbasens `rules` i det formatet som er definert ovenfor\n", "\n", "To hovedmetoder er:\n", - "* `get` for å hente verdien av et attributt, og utføre slutning hvis nødvendig. For eksempel, `get('color')` vil hente verdien av en fargeplass (den vil spørre hvis nødvendig, og lagre verdien for senere bruk i arbeidsminnet). Hvis vi spør `get('color:blue')`, vil den spørre om en farge, og deretter returnere `y`/`n` verdi avhengig av fargen.\n", - "* `eval` utfører den faktiske slutningen, dvs. traverserer AND/OR-treet, evaluerer delmål, osv.\n" + "* `get` for å hente verdien av et attributt, og utføre inferens om nødvendig. For eksempel vil `get('color')` hente verdien for en fargeplass (den vil spørre om nødvendig, og lagre verdien for senere bruk i arbeidsminnet). Hvis vi spør `get('color:blue')`, vil den spørre etter en farge, for så å returnere `y`/`n` avhengig av fargen.\n", + "* `eval` utfører selve inferensen, altså går gjennom AND/OR-treet, evaluerer under-mål, osv.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nå la oss definere vår dyrekunnskapsbase og utføre konsultasjonen. Merk at denne forespørselen vil stille deg spørsmål. Du kan svare ved å skrive `y`/`n` for ja-nei-spørsmål, eller ved å spesifisere et tall (0..N) for spørsmål med lengre svaralternativer.\n" + "La oss nå definere vårt dyrekunnskapsbase og utføre konsultasjonen. Vær oppmerksom på at dette oppkallet vil stille deg spørsmål. Du kan svare ved å skrive `y`/`n` for ja/nei-spørsmål, eller ved å angi tall (0..N) for spørsmål med lengre flervalgssvar.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Bruke PyKnow for Fremoverrettet Infernens\n", + "## Bruke Experta for framoverinferenz\n", "\n", - "I det neste eksempelet skal vi prøve å implementere fremoverrettet inferens ved hjelp av et av bibliotekene for kunnskapsrepresentasjon, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** er et bibliotek for å lage fremoverrettede inferenssystemer i Python, som er designet for å ligne på det klassiske gamle systemet [CLIPS](http://www.clipsrules.net/index.html).\n", + "I det neste eksemplet vil vi prøve å implementere framoverinferenz ved hjelp av et av bibliotekene for kunnskapsrepresentasjon, [Experta](https://github.com/nilp0inter/experta). **Experta** er et bibliotek for å lage framoverinferenzsystemer i Python, som er designet for å være likt det klassiske gamle systemet [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Vi kunne også ha implementert fremoverrettet kjeding selv uten store problemer, men naive implementeringer er vanligvis ikke særlig effektive. For mer effektiv regelmatching brukes en spesiell algoritme, [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Vi kunne også ha implementert framoverkobling selv uten mange problemer, men naive implementasjoner er vanligvis ikke veldig effektive. For mer effektiv regelmatching brukes en spesiell algoritme [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Vi vil definere systemet vårt som en klasse som underklasser `KnowledgeEngine`. Hver regel er definert av en separat funksjon med `@Rule`-annotasjon, som spesifiserer når regelen skal utløses. Inne i regelen kan vi legge til nye fakta ved å bruke `declare`-funksjonen, og å legge til disse faktaene vil føre til at flere regler blir kalt av fremover-slutningsmotoren.\n" + "Vi vil definere systemet vårt som en klasse som underklasser `KnowledgeEngine`. Hver regel defineres av en egen funksjon med `@Rule`-annotasjon, som spesifiserer når regelen skal utløses. Inne i regelen kan vi legge til nye fakta ved hjelp av `declare`-funksjonen, og ved å legge til disse faktaene vil flere regler bli kalt av den fremadrettede inferensmotoren.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Når vi har definert en kunnskapsbase, fyller vi arbeidsminnet vårt med noen innledende fakta, og deretter kaller vi `run()`-metoden for å utføre slutningen. Du kan se som et resultat at nye utledede fakta legges til arbeidsminnet, inkludert det endelige faktumet om dyret (hvis vi har satt opp alle de innledende faktaene riktig).\n" + "Når vi har definert en kunnskapsbase, fyller vi vår arbeidsminne med noen innledende fakta, og deretter kaller vi `run()`-metoden for å utføre inferensen. Du kan se som et resultat at nye utledede fakta legges til i arbeidsminnet, inkludert det endelige faktumet om dyret (hvis vi setter opp alle de innledende faktaene riktig).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Ansvarsfraskrivelse**: \nDette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi streber etter nøyaktighet, vær oppmerksom på at automatiske oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på sitt opprinnelige språk bør anses som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen.\n" + "---\n\n\n**Ansvarsfraskrivelse**:\nDette dokumentet er oversatt ved bruk av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi streber etter nøyaktighet, vennligst vær oppmerksom på at automatiserte oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på dets opprinnelige språk skal betraktes som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi påtar oss ikke ansvar for misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T16:58:48+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T02:51:25+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "no" } diff --git a/translations/no/lessons/2-Symbolic/README.md b/translations/no/lessons/2-Symbolic/README.md index 1f021465..dd5aced2 100644 --- a/translations/no/lessons/2-Symbolic/README.md +++ b/translations/no/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Kunnskapsrepresentasjon og ekspertsystemer +# Kunnskapsrepresentasjon og Ekspertsystemer -![Oppsummering av Symbolic AI-innhold](../../../../translated_images/no/ai-symbolic.715a30cb610411a6.webp) +![Sammendrag av symbolsk KI-innhold](../../../../../../translated_images/no/ai-symbolic.715a30cb610411a6.webp) > Sketchnote av [Tomomi Imura](https://twitter.com/girlie_mac) -Jakten på kunstig intelligens handler om å søke kunnskap for å forstå verden på en måte som ligner på hvordan mennesker gjør det. Men hvordan kan man oppnå dette? +Jakten på kunstig intelligens bygger på et søk etter kunnskap, for å forstå verden på en måte som ligner hvordan mennesker gjør det. Men hvordan kan man gå fram for å gjøre dette? -## [Quiz før forelesning](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Pre-forelesningsquiz](https://ff-quizzes.netlify.app/en/ai/quiz/3) -I AI-forskningens tidlige dager var den top-down tilnærmingen til å skape intelligente systemer (diskutert i forrige leksjon) populær. Ideen var å trekke ut kunnskap fra mennesker i en maskinlesbar form og deretter bruke den til å løse problemer automatisk. Denne tilnærmingen var basert på to store ideer: +I de tidlige dagene av KI var top-down-tilnærmingen til å lage intelligente systemer (diskutert i forrige leksjon) populær. Ideen var å trekke ut kunnskap fra mennesker til en form som maskiner kan lese, og så bruke denne til å automatisk løse problemer. Denne tilnærmingen baserte seg på to store ideer: * Kunnskapsrepresentasjon * Resonnering ## Kunnskapsrepresentasjon -Et av de viktige konseptene i Symbolic AI er **kunnskap**. Det er viktig å skille kunnskap fra *informasjon* eller *data*. For eksempel kan man si at bøker inneholder kunnskap, fordi man kan studere bøker og bli ekspert. Men det bøker faktisk inneholder, kalles *data*, og ved å lese bøker og integrere denne dataen i vår verdensmodell, konverterer vi data til kunnskap. +Et av de viktige konseptene i symbolsk KI er **kunnskap**. Det er viktig å skille kunnskap fra *informasjon* eller *data*. For eksempel kan man si at bøker inneholder kunnskap, fordi man kan studere bøker og bli ekspert. Men det bøkene faktisk inneholder kalles *data*, og ved å lese bøker og integrere disse dataene i vår verdensmodell konverterer vi data til kunnskap. -> ✅ **Kunnskap** er noe som finnes i hodet vårt og representerer vår forståelse av verden. Den oppnås gjennom en aktiv **læringsprosess**, som integrerer informasjonsbiter vi mottar inn i vår aktive modell av verden. +> ✅ **Kunnskap** er noe som finnes i hodet vårt og representerer vår forståelse av verden. Det oppnås gjennom en aktiv **lærings**prosess, som integrerer informasjon vi mottar i vår aktive modell av verden. -Ofte definerer vi ikke kunnskap strengt, men vi knytter det til andre relaterte konsepter ved hjelp av [DIKW-pyramiden](https://en.wikipedia.org/wiki/DIKW_pyramid). Den inneholder følgende konsepter: +Ofte definerer vi ikke kunnskap strengt, men vi plasserer den i forhold til andre beslektede konsepter ved hjelp av [DIKW-pyramiden](https://en.wikipedia.org/wiki/DIKW_pyramid). Den inneholder følgende konsepter: -* **Data** er noe som er representert på fysisk media, som skrevet tekst eller talte ord. Data eksisterer uavhengig av mennesker og kan overføres mellom personer. +* **Data** er noe som er representert i fysisk medium, som skrevet tekst eller talte ord. Data eksisterer uavhengig av mennesker og kan overføres mellom folk. * **Informasjon** er hvordan vi tolker data i hodet vårt. For eksempel, når vi hører ordet *datamaskin*, har vi en viss forståelse av hva det er. -* **Kunnskap** er informasjon som er integrert i vår verdensmodell. For eksempel, når vi lærer hva en datamaskin er, begynner vi å få ideer om hvordan den fungerer, hva den koster, og hva den kan brukes til. Dette nettverket av sammenhengende konsepter utgjør vår kunnskap. -* **Visdom** er enda et nivå av vår forståelse av verden, og det representerer *meta-kunnskap*, f.eks. en forståelse av hvordan og når kunnskapen bør brukes. +* **Kunnskap** er informasjon som integreres i vår verdensmodell. For eksempel, når vi lærer hva en datamaskin er, begynner vi å få ideer om hvordan den fungerer, hvor mye den koster, og hva den kan brukes til. Dette nettverket av sammenhengende konsepter utgjør vår kunnskap. +* **Visdom** er enda et nivå av vår forståelse av verden, og representerer *meta-kunnskap*, f.eks. en oppfatning av hvordan og når kunnskap bør brukes. - + *Bilde [fra Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), av Longlivetheux - Eget arbeid, CC BY-SA 4.0* Dermed er problemet med **kunnskapsrepresentasjon** å finne en effektiv måte å representere kunnskap inne i en datamaskin i form av data, slik at den kan brukes automatisk. Dette kan sees som et spektrum: -![Spektrum for kunnskapsrepresentasjon](../../../../translated_images/no/knowledge-spectrum.b60df631852c0217.webp) +![Kunnskapsrepresentasjonsspektrum](../../../../../../translated_images/no/knowledge-spectrum.b60df631852c0217.webp) > Bilde av [Dmitry Soshnikov](http://soshnikov.com) -* Til venstre finner vi svært enkle typer kunnskapsrepresentasjoner som kan brukes effektivt av datamaskiner. Den enkleste er algoritmisk, der kunnskap er representert av et dataprogram. Dette er imidlertid ikke den beste måten å representere kunnskap på, fordi det ikke er fleksibelt. Kunnskap i hodet vårt er ofte ikke-algoritmisk. -* Til høyre finner vi representasjoner som naturlig tekst. Dette er den mest kraftfulle formen, men den kan ikke brukes til automatisk resonnering. +* Til venstre finnes svært enkle typer kunnskapsrepresentasjoner som effektivt kan brukes av datamaskiner. Den enkleste er algoritmisk, hvor kunnskapen representeres av et dataprogram. Dette er imidlertid ikke den beste måten å representere kunnskap på, fordi det ikke er fleksibelt. Kunnskap i hodet vårt er ofte ikke-algoritmisk. +* Til høyre finnes representasjoner som naturlig tekst. Det er den mest kraftfulle, men kan ikke brukes til automatisk resonnement. -> ✅ Tenk et øyeblikk på hvordan du representerer kunnskap i hodet ditt og konverterer det til notater. Finnes det et spesielt format som fungerer godt for deg for å hjelpe med å huske? +> ✅ Tenk et øyeblikk over hvordan du representerer kunnskap i hodet ditt og konverterer det til notater. Er det et spesielt format som fungerer godt for deg i forhold til å huske? ## Klassifisering av datamaskinens kunnskapsrepresentasjoner -Vi kan klassifisere ulike metoder for kunnskapsrepresentasjon i datamaskiner i følgende kategorier: +Vi kan klassifisere forskjellige metoder for kunnskapsrepresentasjon i datamaskiner i følgende kategorier: -* **Nettverksrepresentasjoner** er basert på det faktum at vi har et nettverk av sammenhengende konsepter i hodet vårt. Vi kan prøve å gjenskape de samme nettverkene som en graf i en datamaskin - et såkalt **semantisk nettverk**. +* **Nettverksrepresentasjoner** baseres på at vi har et nettverk av relaterte konsepter i hodet. Vi kan forsøke å gjenskape de samme nettverkene som en graf inne i datamaskinen – et såkalt **semantisk nettverk**. -1. **Objekt-Attributt-Verdi tripletter** eller **attributt-verdi par**. Siden en graf kan representeres i en datamaskin som en liste over noder og kanter, kan vi representere et semantisk nettverk som en liste over tripletter som inneholder objekter, attributter og verdier. For eksempel kan vi bygge følgende tripletter om programmeringsspråk: +1. **Objekt-Attributt-Verdi-tripletter** eller **attributt-verdi-par**. Siden en graf kan representeres i en datamaskin som en liste over noder og kanter, kan vi representere et semantisk nettverk som en liste over tripletter som inneholder objekter, attributter og verdier. For eksempel kan vi lage følgende tripletter om programmeringsspråk: Objekt | Attributt | Verdi -------|-----------|------ Python | er | Utypet-språk Python | oppfunnet-av | Guido van Rossum Python | blokk-syntaks | innrykk -Utypet-språk | har ikke | typedefinisjoner +Utypet-språk | har-ikke | typedefinisjoner > ✅ Tenk på hvordan tripletter kan brukes til å representere andre typer kunnskap. -2. **Hierarkiske representasjoner** understreker det faktum at vi ofte lager en hierarki av objekter i hodet vårt. For eksempel vet vi at kanarifugl er en fugl, og alle fugler har vinger. Vi har også en idé om hvilken farge en kanarifugl vanligvis har, og hva flyhastigheten deres er. +2. **Hierarkiske representasjoner** understreker at vi ofte skaper et hierarki av objekter i hodet vårt. For eksempel vet vi at kanarifugl er en fugl, og alle fugler har vinger. Vi har også en idé om hvilken farge en kanarifugl vanligvis har, og hva flygehastigheten deres er. - - **Rammerepresentasjon** er basert på å representere hvert objekt eller klasse av objekter som en **ramme** som inneholder **slisser**. Slisser har mulige standardverdier, verdibegrensninger eller lagrede prosedyrer som kan kalles for å hente verdien av en slisse. Alle rammer danner et hierarki som ligner på et objekthierarki i objektorienterte programmeringsspråk. - - **Scenarier** er spesielle typer rammer som representerer komplekse situasjoner som kan utfolde seg over tid. + - **Ramme-representasjon** baserer seg på å representere hvert objekt eller klasse av objekter som en **ramme** som inneholder **felt**. Feltene kan ha mulige standardverdier, verdi-restriksjoner, eller lagrede prosedyrer som kan kalles for å hente verdien av et felt. Alle rammene danner et hierarki lik et objekthierarki i objektorienterte programmeringsspråk. + - **Scenarier** er en spesiell type rammer som representerer komplekse situasjoner som kan utfolde seg over tid. **Python** -Slisse | Verdi | Standardverdi | Intervall | --------|-------|---------------|----------| +Felt | Verdi | Standardverdi | Intervall | +-----|-------|---------------|----------| Navn | Python | | | -Er-A | Utypet-språk | | | -Variabel Case | | CamelCase | | +Er-En | Utypet-språk | | | +Variabelskriving | | CamelCase | | Programlengde | | | 5-5000 linjer | Blokk-syntaks | Innrykk | | | -3. **Prosedyrerepresentasjoner** er basert på å representere kunnskap som en liste over handlinger som kan utføres når en viss betingelse oppstår. - - Produksjonsregler er hvis-da-utsagn som lar oss trekke konklusjoner. For eksempel kan en lege ha en regel som sier at **HVIS** en pasient har høy feber **ELLER** høyt nivå av C-reaktivt protein i blodprøven **DA** har han en betennelse. Når vi møter en av betingelsene, kan vi trekke en konklusjon om betennelse og deretter bruke den i videre resonnering. - - Algoritmer kan betraktes som en annen form for prosedyrerepresentasjon, selv om de nesten aldri brukes direkte i kunnskapsbaserte systemer. +3. **Prosedyremessige representasjoner** bygger på å representere kunnskap som en liste over handlinger som kan utføres når en viss betingelse inntreffer. + - Produksjonsregler er hvis-da-setninger som lar oss trekke konklusjoner. For eksempel kan en lege ha en regel som sier at **HVIS** en pasient har høy feber **ELLER** høyt nivå av C-reaktivt protein i blodprøve **SÅ** har han en inflamasjon. Når vi møter en av betingelsene, kan vi trekke en konklusjon om betennelse og bruke dette i videre resonnement. + - Algoritmer kan betraktes som en annen form for prosedyremessig representasjon, selv om de nesten aldri brukes direkte i kunnskapsbaserte systemer. -4. **Logikk** ble opprinnelig foreslått av Aristoteles som en måte å representere universell menneskelig kunnskap på. - - Predikatlogikk som en matematisk teori er for rik til å være beregningsbar, derfor brukes vanligvis en delmengde av den, som Horn-klausuler brukt i Prolog. - - Beskrivende logikk er en familie av logiske systemer som brukes til å representere og resonnere om hierarkier av objekter i distribuerte kunnskapsrepresentasjoner som *semantisk web*. +4. **Logikk** ble opprinnelig foreslått av Aristoteles som en måte å representere allmenn menneskelig kunnskap på. + - Predikatlogikk som en matematisk teori er for rik til å være beregnbar, derfor brukes vanligvis et delsett, som Horn-klausuler brukt i Prolog. + - Beskrivende logikk er en familie av logiske systemer brukt til å representere og resonnere om hierarkier av objekter i distribuerte kunnskapsrepresentasjoner som *semantisk web*. ## Ekspertsystemer -En av de tidlige suksessene til symbolsk AI var de såkalte **ekspertsystemene** - datasystemer som var designet for å fungere som en ekspert innenfor et begrenset problemområde. De var basert på en **kunnskapsbase** hentet fra en eller flere menneskelige eksperter, og de inneholdt en **slutningsmotor** som utførte resonnering basert på denne kunnskapen. +En av de tidlige suksessene innen symbolsk KI var såkalte **ekspertsystemer** – datasystemer som var designet for å opptre som eksperter i et begrenset problemområde. De baserte seg på en **kunnskapsbase** hentet fra en eller flere menneskelige eksperter, og de inneholdt en **begrunnelsesmotor** som utførte resonnering på toppen av dette. -![Menneskelig arkitektur](../../../../translated_images/no/arch-human.5d4d35f1bba3ab1c.webp) | ![Kunnskapsbasert system](../../../../translated_images/no/arch-kbs.3ec5c150b09fa8da.webp) +![Menneskelig arkitektur](../../../../../../translated_images/no/arch-human.5d4d35f1bba3ab1c.webp) | ![Kunnskapsbasert system](../../../../../../translated_images/no/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Forenklet struktur av et menneskelig nervesystem | Arkitektur av et kunnskapsbasert system +Forenklet struktur av menneskelig nervesystem | Arkitektur for et kunnskapsbasert system -Ekspertsystemer er bygget som det menneskelige resonnanssystemet, som inneholder **korttidsminne** og **langtidsminne**. Tilsvarende skiller vi i kunnskapsbaserte systemer mellom følgende komponenter: +Ekspertsystemer er bygd som det menneskelige resonnementssystemet, som inneholder **korttidshukommelse** og **langtidshukommelse**. På samme måte skiller vi i kunnskapsbaserte systemer mellom følgende komponenter: -* **Probleminnhold**: inneholder kunnskapen om problemet som for øyeblikket løses, f.eks. temperaturen eller blodtrykket til en pasient, om han har betennelse eller ikke, osv. Denne kunnskapen kalles også **statisk kunnskap**, fordi den inneholder et øyeblikksbilde av hva vi for øyeblikket vet om problemet - den såkalte *problemtilstanden*. -* **Kunnskapsbase**: representerer langtidskunnskap om et problemområde. Den hentes manuelt fra menneskelige eksperter og endres ikke fra konsultasjon til konsultasjon. Fordi den lar oss navigere fra én problemtilstand til en annen, kalles den også **dynamisk kunnskap**. -* **Slutningsmotor**: organiserer hele prosessen med å søke i problemtilstandsrommet, stille spørsmål til brukeren når det er nødvendig. Den er også ansvarlig for å finne de riktige reglene som skal brukes i hver tilstand. +* **Problemhukommelse**: inneholder kunnskapen om problemet som løses for øyeblikket, f.eks. temperaturen eller blodtrykket til en pasient, om han har betennelse eller ikke, osv. Denne kunnskapen kalles også **statisk kunnskap**, fordi den inneholder et øyeblikksbilde av hva vi for øyeblikket vet om problemet – den såkalte *problemtilstanden*. +* **Kunnskapsbase**: representerer langtidshukommelsen om et problemområde. Den trekkes ut manuelt fra menneskelige eksperter, og endres ikke fra konsultasjon til konsultasjon. Fordi den lar oss navigere fra ett problemtilstand til et annet, kalles den også **dynamisk kunnskap**. +* **Begrunnelsesmotor**: orkestrerer hele prosessen med å søke i problemtilstandsrommet, stiller spørsmål til brukeren når det trengs. Den er også ansvarlig for å finne rette regler som skal anvendes i hver tilstand. -Som et eksempel, la oss se på følgende ekspertsystem for å bestemme et dyr basert på dets fysiske egenskaper: +Som eksempel kan vi se på følgende ekspertsystem for å bestemme et dyr basert på dets fysiske egenskaper: -![AND-OR-tre](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.webp) +![OG-ELLER-tre](../../../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.webp) > Bilde av [Dmitry Soshnikov](http://soshnikov.com) -Dette diagrammet kalles et **AND-OR-tre**, og det er en grafisk representasjon av et sett med produksjonsregler. Å tegne et tre er nyttig i begynnelsen av å hente kunnskap fra eksperten. For å representere kunnskapen i datamaskinen er det mer praktisk å bruke regler: +Dette diagrammet kalles et **AND-OR-tre**, og det er en grafisk representasjon av et sett produksjonsregler. Å tegne et tre er nyttig i begynnelsen av kunnskapsekstraksjon fra eksperten. For å representere kunnskapen i datamaskinen er det mer praktisk å bruke regler: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Du kan legge merke til at hver betingelse på venstre side av regelen og handlingen i hovedsak er objekt-attributt-verdi (OAV) tripletter. **Arbeidsminne** inneholder settet med OAV-tripletter som tilsvarer problemet som for øyeblikket løses. En **regelmotor** ser etter regler der en betingelse er oppfylt og anvender dem, og legger til en ny triplet i arbeidsminnet. +Du kan legge merke til at hver betingelse på venstre side og handlingen i regelen egentlig er objekt-attribute-verdi (OAV) tripletter. **Arbeidshukommelsen** inneholder settet av OAV-tripletter som tilsvarer det problemet som løses for øyeblikket. En **regelmotor** søker etter regler hvor en betingelse er oppfylt og anvender dem, legger til en ny triplet i arbeidshukommelsen. -> ✅ Lag ditt eget AND-OR-tre om et emne du liker! +> ✅ Skriv ditt eget AND-OR-tre om et emne du liker! -### Fremover- vs. bakoverresonnering +### Fremover- vs. Bakoverresonnering -Prosessen beskrevet ovenfor kalles **fremoverresonnering**. Den starter med noen innledende data om problemet tilgjengelig i arbeidsminnet, og deretter utfører den følgende resonnanssløyfe: +Prosessen beskrevet ovenfor kalles **fremoverresonnering**. Den starter med noen innledende data om problemet som finnes i arbeidshukommelsen, og utfører deretter følgende resonnementsløkke: -1. Hvis mål-attributtet er til stede i arbeidsminnet - stopp og gi resultatet -2. Se etter alle regler der betingelsen for øyeblikket er oppfylt - oppnå **konfliktsett** av regler. -3. Utfør **konfliktløsning** - velg én regel som skal utføres i dette trinnet. Det kan være ulike strategier for konfliktløsning: +1. Hvis målattributtet finnes i arbeidshukommelsen – stopp og gi resultat +2. Se etter alle regler hvor betingelsen er oppfylt nå – opprett **konfliktsett** av regler. +3. Utfør **konfliktløsning** – velg én regel som skal kjøres i dette steget. Det kan være forskjellige strategier for konfliktløsning: - Velg den første anvendelige regelen i kunnskapsbasen - Velg en tilfeldig regel - - Velg en *mer spesifikk* regel, dvs. den som oppfyller flest betingelser på venstre side (LHS) -4. Anvend valgt regel og sett inn ny kunnskap i problemtilstanden -5. Gjenta fra trinn 1. + - Velg en *mer spesifikk* regel, dvs. den som møter flest betingelser på venstresiden (LHS) +4. Utfør valgt regel og sett inn ny kunnskap i problemtilstanden +5. Gjenta fra steg 1. -I noen tilfeller ønsker vi imidlertid å starte med tom kunnskap om problemet og stille spørsmål som hjelper oss å komme frem til en konklusjon. For eksempel, når vi stiller en medisinsk diagnose, utfører vi vanligvis ikke alle medisinske analyser på forhånd før vi begynner å diagnostisere pasienten. Vi ønsker heller å utføre analyser når en beslutning må tas. +I noen tilfeller ønsker vi imidlertid å starte med tom kunnskap om problemet, og stille spørsmål som hjelper oss å komme fram til konklusjonen. For eksempel ved medisinsk diagnostikk utfører man vanligvis ikke alle medisinske analyser på forhånd før man begynner å diagnostisere pasienten. Vi ønsker heller å utføre analyser når en beslutning må tas. -Denne prosessen kan modelleres ved hjelp av **bakoverresonnering**. Den drives av **målet** - attributtverdien vi prøver å finne: +Denne prosessen kan modelleres med **bakoverresonnering**. Den styres av **målet** – attributtverdien vi prøver å finne: -1. Velg alle regler som kan gi oss verdien av et mål (dvs. med målet på høyre side (RHS)) - et konfliktsett -1. Hvis det ikke finnes regler for dette attributtet, eller det finnes en regel som sier at vi bør spørre brukeren om verdien - spør om det, ellers: -1. Bruk konfliktløsningsstrategi for å velge én regel som vi vil bruke som *hypotese* - vi vil prøve å bevise den -1. Gjenta prosessen rekursivt for alle attributter på venstre side av regelen, og prøv å bevise dem som mål -1. Hvis prosessen mislykkes på noe tidspunkt - bruk en annen regel i trinn 3. +1. Velg alle regler som kan gi oss verdien til et mål (dvs. med målet på høyresiden (RHS)) – konfliktsett +1. Hvis det ikke finnes regler for dette attributtet, eller det finnes en regel som sier at vi skal spørre brukeren om verdien – spør om den, ellers: +1. Bruk konfliktløsningsstrategi for å velge en regel som vi bruker som *hypotese* – vi prøver å bevise den +1. Gjenta prosessen rekursivt for alle attributter på LHS av regelen, prøv å bevise dem som mål +1. Hvis prosessen feiler når som helst – bruk en annen regel på steg 3. -> ✅ I hvilke situasjoner er fremoverresonnering mer passende? Hva med bakoverresonnering? +> ✅ I hvilke situasjoner er fremoverresonnering mer hensiktsmessig? Hva med bakoverresonnering? -### Implementering av ekspertsystemer +### Implementering av Ekspertsystemer -Ekspertsystemer kan implementeres ved hjelp av ulike verktøy: +Ekspertsystemer kan implementeres med ulike verktøy: -* Programmere dem direkte i et høynivå programmeringsspråk. Dette er ikke den beste ideen, fordi hovedfordelen med et kunnskapsbasert system er at kunnskap er adskilt fra resonnering, og potensielt bør en ekspert på problemområdet kunne skrive regler uten å forstå detaljene i resonneringsprosessen. -* Bruke en **ekspertsystemskall**, dvs. et system spesielt designet for å fylles med kunnskap ved hjelp av et kunnskapsrepresentasjonsspråk. +* Programmering direkte i et høynivå programmeringsspråk. Dette er ikke den beste ideen, fordi hovedfordelen med et kunnskapsbasert system er at kunnskapen er separert fra resonnementet, og potensielt bør en ekspert innen problemdomenet kunne skrive regler uten å forstå detaljene i resonnementprosessen. +* Bruke en **ekspertsystem-skall**, dvs. et system spesielt designet for å fylles med kunnskap ved bruk av et kunnskapsrepresentasjonsspråk. -## ✍️ Øvelse: Dyreslutning +## ✍️ Øvelse: Dyreresonnering -Se [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) for et eksempel på implementering av fremover- og bakoverresonnering i et ekspertsystem. +Se [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) for et eksempel på implementering av fremover- og bakoverresonnerende ekspertsystem. -> **Merk**: Dette eksemplet er ganske enkelt og gir bare en idé om hvordan et ekspertsystem ser ut. Når du begynner å lage et slikt system, vil du bare merke noe *intelligent* oppførsel fra det når du når et visst antall regler, rundt 200+. På et tidspunkt blir reglene for komplekse til å holde alle i hodet, og da kan du begynne å lure på hvorfor systemet tar visse beslutninger. Men en viktig egenskap ved kunnskapsbaserte systemer er at du alltid kan *forklare* nøyaktig hvordan noen av beslutningene ble tatt. +> **Merk**: Dette eksempelet er ganske enkelt, og gir bare en idé om hvordan et ekspertsystem ser ut. Når du starter å lage slike systemer, merker du først *intelligent* oppførsel når antall regler når et visst nivå, rundt 200+. På et tidspunkt blir regler for komplekse til å holde oversikt over alle i hodet, og da kan du begynne å lure på hvorfor systemet tar visse beslutninger. Den viktige egenskapen ved kunnskapsbaserte systemer er at du alltid kan *forklare* akkurat hvordan enhver beslutning ble tatt. -## Ontologier og det semantiske nettet +## Ontologier og Semantisk Web -På slutten av 1900-tallet var det en initiativ for å bruke kunnskapsrepresentasjon til å annotere internettressurser, slik at det ville være mulig å finne ressurser som samsvarer med svært spesifikke forespørsler. Denne bevegelsen ble kalt **Semantisk Web**, og den var basert på flere konsepter: +På slutten av 1900-tallet var det et initiativ for å bruke kunnskapsrepresentasjon for å annotere internettressurser, slik at det ble mulig å finne ressurser som svarer til svært spesifikke spørsmål. Dette initiativet ble kalt **Semantisk Web**, og det bygde på flere konsepter: -- En spesiell kunnskapsrepresentasjon basert på **[beskrivende logikk](https://en.wikipedia.org/wiki/Description_logic)** (DL). Den ligner på rammebasert kunnskapsrepresentasjon, fordi den bygger et hierarki av objekter med egenskaper, men den har formell logisk semantikk og resonnering. Det finnes en hel familie av DL-er som balanserer mellom uttrykksevne og algoritmisk kompleksitet i resonnering. -- Distribuert kunnskapsrepresentasjon, der alle konsepter er representert av en global URI-identifikator, noe som gjør det mulig å lage kunnskapshierarkier som spenner over internett. +- En spesiell kunnskapsrepresentasjon basert på **[beskrivende logikker](https://en.wikipedia.org/wiki/Description_logic)** (DL). Den ligner på rammebasert kunnskapsrepresentasjon, fordi den bygger et hierarki av objekter med egenskaper, men har formell logisk semantikk og resonnement. Det finnes en hel familie av DL-er som balanserer mellom uttrykksevne og algoritmisk kompleksitet ved resonnement. +- Distribuert kunnskapsrepresentasjon, der alle konsepter representeres av en global URI-identifikator, som gjør det mulig å skape kunnskapshierarkier som spenner over internett. - En familie av XML-baserte språk for kunnskapsbeskrivelse: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Et kjernebegrep i den semantiske webben er begrepet **Ontologi**. Det refererer til en eksplisitt spesifikasjon av et problemområde ved bruk av en formell kunnskapsrepresentasjon. Den enkleste ontologien kan være en hierarki av objekter i et problemområde, men mer komplekse ontologier vil inkludere regler som kan brukes til å trekke slutninger. +Et kjernebegrep i det semantiske nett er et begrep om **ontologi**. Det refererer til en eksplisitt spesifikasjon av et problemområde ved bruk av en formell kunnskapsrepresentasjon. Den enkleste ontologien kan bare være en hierarki av objekter i et problemområde, men mer komplekse ontologier inkluderer regler som kan brukes til slutning. -I den semantiske webben er alle representasjoner basert på tripletter. Hvert objekt og hver relasjon er unikt identifisert av en URI. For eksempel, hvis vi ønsker å uttrykke at dette AI-lærematerialet ble utviklet av Dmitry Soshnikov den 1. januar 2022, kan vi bruke følgende tripletter: +I det semantiske nettet er alle representasjoner basert på tripletter. Hvert objekt og hver relasjon identifiseres entydig med URI. For eksempel, hvis vi ønsker å angi fakta at dette AI-læreplanen har blitt utviklet av Dmitry Soshnikov 1. januar 2022 – her er triplettene vi kan bruke: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Her er `http://www.example.com/terms/creation-date` og `http://purl.org/dc/elements/1.1/creator` noen velkjente og universelt aksepterte URI-er for å uttrykke begrepene *skaper* og *opprettelsesdato*. +> ✅ Her `http://www.example.com/terms/creation-date` og `http://purl.org/dc/elements/1.1/creator` er noen kjente og universelt aksepterte URI-er for å uttrykke begrepene *skaper* og *opprettelsesdato*. I et mer komplekst tilfelle, hvis vi ønsker å definere en liste over skapere, kan vi bruke noen datastrukturer definert i RDF. - + -> Diagrammene ovenfor av [Dmitry Soshnikov](http://soshnikov.com) +> Diagrammene over av [Dmitry Soshnikov](http://soshnikov.com) -Fremgangen med å bygge den semantiske webben ble på en måte bremset av suksessen til søkemotorer og teknikker for naturlig språkbehandling, som gjør det mulig å trekke ut strukturert data fra tekst. Imidlertid er det fortsatt betydelige innsatsområder for å opprettholde ontologier og kunnskapsbaser. Noen prosjekter verdt å merke seg: +Fremgangen med å bygge det semantiske nettet ble på en måte bremset av suksessen til søkemotorer og naturlige språkprosesseringsteknikker, som tillater utvinning av strukturert data fra tekst. Imidlertid er det fortsatt betydelige innsatsområder for å opprettholde ontologier og kunnskapsbaser. Noen få prosjekter verdt å nevne: -* [WikiData](https://wikidata.org/) er en samling av maskinlesbare kunnskapsbaser knyttet til Wikipedia. Mesteparten av dataene er hentet fra Wikipedia *InfoBoxes*, deler av strukturert innhold inne i Wikipedia-sider. Du kan [spørre](https://query.wikidata.org/) WikiData i SPARQL, et spesielt spørrespråk for den semantiske webben. Her er et eksempel på en forespørsel som viser de mest populære øyefargene blant mennesker: +* [WikiData](https://wikidata.org/) er en samling av maskinlesbare kunnskapsbaser tilknyttet Wikipedia. Mesteparten av dataene blir hentet fra Wikipedia *InfoBoxes*, biter av strukturert innhold inne i Wikipedia-sider. Du kan [spørrer](https://query.wikidata.org/) wikidata i SPARQL, et spesielt spørringsspråk for det semantiske nettet. Her er et eksempel på en spørring som viser de mest populære øyenfargene blant mennesker: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) er et annet initiativ som ligner på WikiData. +* [DBpedia](https://www.dbpedia.org/) er en annen innsats lik WikiData. -> ✅ Hvis du vil eksperimentere med å bygge dine egne ontologier, eller åpne eksisterende, finnes det en flott visuell ontologiredigerer kalt [Protégé](https://protege.stanford.edu/). Last den ned, eller bruk den online. +> ✅ Hvis du ønsker å eksperimentere med å bygge dine egne ontologier, eller åpne eksisterende, finnes det en flott visuell ontologiredigerer kalt [Protégé](https://protege.stanford.edu/). Last det ned, eller bruk det på nett. - + -*Web Protégé-redigerer åpnet med Romanov-familieontologien. Skjermbilde av Dmitry Soshnikov* +*Web Protégé-redigering åpnet med Romanov-familiens ontologi. Skjermbilde av Dmitry Soshnikov* ## ✍️ Øvelse: En familieontologi -Se [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) for et eksempel på bruk av semantiske webteknikker for å resonnere om familierelasjoner. Vi vil ta et slektstre representert i det vanlige GEDCOM-formatet og en ontologi for familierelasjoner og bygge en graf over alle familierelasjoner for et gitt sett med individer. +Se [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) for et eksempel på bruk av teknikker fra det semantiske nettet for å slutte om familierelasjoner. Vi tar et familietre representert i vanlig GEDCOM-format og en ontologi av familierelasjoner, og bygger en graf av alle familierelasjoner for en gitt gruppe individer. ## Microsoft Concept Graph -I de fleste tilfeller blir ontologier nøye laget for hånd. Det er imidlertid også mulig å **utvinne** ontologier fra ustrukturert data, for eksempel fra tekster i naturlig språk. +I de fleste tilfeller blir ontologier nøye laget for hånd. Det er imidlertid også mulig å **utvinne** ontologier fra ustrukturerte data, for eksempel fra naturlige språktekster. -En slik innsats ble gjort av Microsoft Research, og resulterte i [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Et slikt forsøk ble gjort av Microsoft Research, og resulterte i [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Det er en stor samling av enheter gruppert sammen ved bruk av `is-a` arv-relasjoner. Den gjør det mulig å svare på spørsmål som "Hva er Microsoft?" - svaret kan være noe som "et selskap med sannsynlighet 0.87, og et merke med sannsynlighet 0.75". +Det er en stor samling entiteter gruppert sammen ved hjelp av `is-a` arverelasjon. Det muliggjør svar på spørsmål som "Hva er Microsoft?" - svaret kan være noe slikt som "et selskap med sannsynlighet 0,87, og et merke med sannsynlighet 0,75". -Grafen er tilgjengelig enten som REST API, eller som en stor nedlastbar tekstfil som lister opp alle enhetspar. +Grafen er tilgjengelig enten som REST API, eller som en stor nedlastbar tekstfil som lister alle entitetsparet. -## ✍️ Øvelse: En konseptgraf +## ✍️ Øvelse: Et konseptkart -Prøv [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb)-notatboken for å se hvordan vi kan bruke Microsoft Concept Graph til å gruppere nyhetsartikler i flere kategorier. +Prøv [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notatboken for å se hvordan vi kan bruke Microsoft Concept Graph til å gruppere nyhetsartikler i flere kategorier. ## Konklusjon -I dag blir AI ofte betraktet som et synonym for *Maskinlæring* eller *Nevrale nettverk*. Imidlertid viser et menneske også eksplisitt resonnement, noe som for øyeblikket ikke håndteres av nevrale nettverk. I virkelige prosjekter brukes eksplisitt resonnement fortsatt til å utføre oppgaver som krever forklaringer, eller evnen til å endre systemets oppførsel på en kontrollert måte. +I dag blir AI ofte ansett som synonymt med *maskinlæring* eller *nevrale nettverk*. Imidlertid utviser også et menneske eksplisitt resonnement, noe som for øyeblikket ikke håndteres av nevrale nettverk. I virkelige prosjekter brukes eksplisitt resonnement fortsatt for å utføre oppgaver som krever forklaringer, eller å kunne modifisere oppførselen til systemet på en kontrollert måte. ## 🚀 Utfordring -I Family Ontology-notatboken knyttet til denne leksjonen, er det en mulighet til å eksperimentere med andre familierelasjoner. Prøv å oppdage nye forbindelser mellom personer i slektstreet. +I Familieontologi-notatboken tilknyttet denne leksjonen, finnes det mulighet til å eksperimentere med andre familierelasjoner. Prøv å oppdage nye forbindelser mellom mennesker i familietreet. -## [Quiz etter forelesning](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Quiz etter forelesningen](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## Gjennomgang & Selvstudium -Gjør litt research på internett for å oppdage områder der mennesker har forsøkt å kvantifisere og kodifisere kunnskap. Ta en titt på Blooms taksonomi, og gå tilbake i historien for å lære hvordan mennesker har forsøkt å forstå verden. Utforsk arbeidet til Linnaeus for å lage en taksonomi av organismer, og observer hvordan Dmitri Mendeleev skapte en måte for kjemiske elementer å bli beskrevet og gruppert. Hvilke andre interessante eksempler kan du finne? +Gjør noen undersøkelser på internett for å oppdage områder hvor mennesker har forsøkt å kvantifisere og kodifisere kunnskap. Ta en titt på Blooms taksonomi, og gå tilbake i historien for å lære hvordan mennesker prøvde å forstå verden de levde i. Utforsk arbeidet til Linnaeus for å lage en taksonomi av organismer, og observer måten Dmitri Mendeleev skapte en metode for at kjemiske elementer skulle kunne beskrives og grupperes. Hvilke andre interessante eksempler kan du finne? **Oppgave**: [Bygg en ontologi](assignment.md) --- + +**Ansvarsfraskrivelse**: +Dette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi streber etter nøyaktighet, vennligst vær oppmerksom på at automatiske oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på det opprinnelige språket skal betraktes som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for eventuelle misforståelser eller feiltolkninger som følge av bruk av denne oversettelsen. + \ No newline at end of file