diff --git a/README.md b/README.md index a3f63386..d0231b17 100644 --- a/README.md +++ b/README.md @@ -40,52 +40,52 @@ For a gentle introduction to *AI in the Cloud* topics you may consider taking th NoLessonIntroPyTorchKeras/TensorFlowLab IIntroduction to AIPAT -1Introduction and History of AIText +1Introduction and History of AIText IISymbolic AIPAT -2 Knowledge Representation and Expert SystemsTextExpert System, Ontology, Concept Graph -IIIIntroduction to Neural NetworksPAT +2 Knowledge Representation and Expert SystemsTextExpert System, Ontology, Concept Graph +IIIIntroduction to Neural NetworksPAT 3Perceptron - Text - NotebookLab -4 Multi-Layered Perceptron and Creating our own FrameworkTextNotebookLab + Text + NotebookLab +4 Multi-Layered Perceptron and Creating our own FrameworkTextNotebookLab 5 Intro to Frameworks (PyTorch/TensorFlow)
Overfitting - Text
Text - PyTorch - Keras/TensorFlow - Lab -IVComputer Vision + Text
Text + PyTorch + Keras/TensorFlow + Lab +IVComputer Vision MS Learn MS Learn PAT 6Intro to Computer Vision. OpenCVTextNotebook -7Convolutional Neural Networks
CNN ArchitecturesText
TextPyTorchTensorFlowLab -8Pre-trained Networks and Transfer Learning
Training TricksText
TextPyTorchTensorFlow
Dropout sampleLab -9Autoencoders and VAEsTextPyTorchTensorFlow -10Generative Adversarial NetworksTextPyTorchTensorFlow +7Convolutional Neural Networks
CNN ArchitecturesText
TextPyTorchTensorFlowLab +8Pre-trained Networks and Transfer Learning
Training TricksText
TextPyTorchTensorFlow
Dropout sampleLab +9Autoencoders and VAEsTextPyTorchTensorFlow +10Generative Adversarial NetworksTextPyTorchTensorFlow 11Object DetectionTextPyTorchTensorFlow -12Semantic Segmentation. U-NetTextPyTorchTensorFlow -VNatural Language Processing +12Semantic Segmentation. U-NetTextPyTorchTensorFlow +VNatural Language Processing MS Learn MS Learn PAT -13Text Representation. Bow/TF-IDFTextPyTorchTensorFlow -14Semantic word embeddings. Word2Vec and GloVeTextPyTorchTensorFlow -15Language Modeling. Training your own embeddingsTextPyTorchTensorFlow -16Recurrent Neural NetworksTextPyTorchTensorFlow -17Generative Recurrent NetworksTextPyTorchTensorFlow -18Transformers. BERT.TextPyTorchTensorFlow +13Text Representation. Bow/TF-IDFTextPyTorchTensorFlow +14Semantic word embeddings. Word2Vec and GloVeTextPyTorchTensorFlow +15Language Modeling. Training your own embeddingsTextPyTorchTensorFlow +16Recurrent Neural NetworksTextPyTorchTensorFlow +17Generative Recurrent NetworksTextPyTorchTensorFlow +18Transformers. BERT.TextPyTorchTensorFlow 19Named Entity RecognitionTextPyTorchTensorFlow 20Large Language Models, Prompt Programming and Few-Shot TasksTextPyTorchTensorFlow VIOther AI TechniquesPAT -21Genetic AlgorithmsTextNotebook -22Deep Reinforcement LearningTextPyTorchTensorFlow -23Multi-Agent SystemsText +21Genetic AlgorithmsTextNotebook +22Deep Reinforcement LearningTextPyTorchTensorFlow +23Multi-Agent SystemsText VIIAI EthicsPAT -24AI Ethics and Responsible AIText +24AI Ethics and Responsible AIText Extras -1Multi-Modal Networks, CLIP and VQGANText +1Multi-Modal Networks, CLIP and VQGANText **[Mindmap of the Course](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** @@ -127,7 +127,7 @@ However, if you would like to take the course as a self-study project, we sugges ## Meet the Team -[![Promo video](./lessons/sketchnotes/ai-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video") +[![Promo video](.lessons/sketchnotes/ai-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video") > 🎥 Click the image above for a video about the project and the folks who created it! diff --git a/lessons/1-Intro/README.md b/lessons/1-Intro/README.md index d0cf425e..e0c64eb5 100644 --- a/lessons/1-Intro/README.md +++ b/lessons/1-Intro/README.md @@ -4,7 +4,7 @@ > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) -## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/1) +## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/101) **Artificial Intelligence** is an exciting scientific discipline that studies how we can make computers exhibit intelligent behavior, e.g. do those things that human beings are good at doing. @@ -139,7 +139,7 @@ Over the past few years we have witnessed huge successes with large language mod Do a tour of the internet to determine where, in your opinion, AI is most effectively used. Is it in a Mapping app, or some speech-to-text service or a video game? Research how the system was built. -## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/2) +## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/201) ## Review & Self Study diff --git a/lessons/2-Symbolic/README.md b/lessons/2-Symbolic/README.md index 59968c8e..421c0b6a 100644 --- a/lessons/2-Symbolic/README.md +++ b/lessons/2-Symbolic/README.md @@ -6,7 +6,7 @@ The quest for artificial intelligence is based on a search for knowledge, to make sense of the world similar to how humans do. But how can you go about doing this? -## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/3) +## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/201) In the early days of AI, the top-down approach to creating intelligent systems (discussed in the previous lesson) was popular. The idea was to extract the knowledge from people into some machine-readable form, and then use it to automatically solve problems. This approach was based on two big ideas: @@ -230,7 +230,7 @@ Nowadays, AI is often considered to be a synonym for *Machine Learning* or *Neur In the Family Ontology notebook associated to this lesson, there is an opportunity to experiment with other family relations. Try to discover new connections between people in the family tree. -## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/4) +## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/202) ## Review & Self Study diff --git a/lessons/3-NeuralNetworks/03-Perceptron/README.md b/lessons/3-NeuralNetworks/03-Perceptron/README.md index 4b37fb89..d2d7790a 100644 --- a/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -1,6 +1,6 @@ # Introduction to Neural Networks: Perceptron -## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/5) +## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/301) One of the first attempts to implement something similar to a modern neural network was done by Frank Rosenblatt from Cornell Aeronautical Laboratory in 1957. It was a hardware implementation called "Mark-1", designed to recognize primitive geometric figures, such as triangles, squares and circles. @@ -76,7 +76,7 @@ In this lesson, you learned about a perceptron, which is a binary classification If you'd like to try to build your own perceptron, try [this lab on Microsoft Learn](https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?WT.mc_id=academic-57639-dmitryso) which uses the [Azure ML designer](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer?WT.mc_id=academic-57639-dmitryso). -## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/6) +## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/302) ## Review & Self Study diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/lessons/3-NeuralNetworks/04-OwnFramework/README.md index d1adbc42..434195e3 100644 --- a/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -10,7 +10,7 @@ In this section we will extend this model into a more flexible framework, allowi We will also develop our own modular framework in Python that will allow us to construct different neural network architectures. -## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/7) +## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/401) ## Formalization of Machine Learning @@ -72,7 +72,7 @@ In the accompanying notebook, you will implement your own framework for building Proceed to the [OwnFramework](OwnFramework.ipynb) notebook and work through it. -## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/8) +## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/402) ## Review & Self Study diff --git a/lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md b/lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md index c0be8328..f49989a9 100644 --- a/lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md +++ b/lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md @@ -51,7 +51,7 @@ In this lesson, you learned about the differences between the various APIs for t In the accompanying notebooks, you will find 'tasks' at the bottom; work through the notebooks and complete the tasks. -## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/10) +## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/502) ## Review & Self Study diff --git a/lessons/3-NeuralNetworks/05-Frameworks/README.md b/lessons/3-NeuralNetworks/05-Frameworks/README.md index 1bf6e0f1..14c6b0a6 100644 --- a/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -5,7 +5,7 @@ As we have learned already, to be able to train neural networks efficiently we n * To operate on tensors, eg. to multiply, add, and compute some functions such as sigmoid or softmax * To compute gradients of all expressions, in order to perform gradient descent optimization -## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/9) +## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/501) While the `numpy` library can do the first part, we need some mechanism to compute gradients. In [our framework](../04-OwnFramework/OwnFramework.ipynb) that we have developed in the previous section we had to manually program all derivative functions inside the `backward` method, which does backpropagation. Ideally, a framework should give us the opportunity to compute gradients of *any expression* that we can define.