Merge pull request #56 from jlooper/main

rearranging the repos to flatten them out

.github/workflows/docsify.yml

@@ -1,16 +0,0 @@
-
-name: docs
-
-on: [push]
-
-jobs:
-  build-docs:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@master
-    - uses: tool3/docsify-action@master
-      with:
-        github_token: ${{ secrets.GP_TOKEN }}
-        dir: etc/docsify
-        # destination branch
-

README.md

@@ -10,7 +10,7 @@
 
 # Artificial Intelligence for Beginners - A Curriculum
 
-> **This curriculum is being actively developed on GitHub. Look into [contributing](CONTRIBUTING.md) to see which areas require active contributions. Please consider this a pre-release, and do not actively use in the classroom yet!**
+> **This curriculum is being actively developed on GitHub. Look into [contributing](/etc/CONTRIBUTING.md) to see which areas require active contributions. Please consider this a pre-release, and do not actively use in the classroom yet!**
 
 
 Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Artificial Intelligence**. 
@@ -27,7 +27,7 @@ What we will not cover in this curriculum:
 * Business cases of using **AI in Business**. Consider taking [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-57639-dmitryso) learning path on Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-57639-dmitryso), developed in cooperation with [INSEAD](https://www.insead.edu/).
 * **Classic Machine Learning**, which is well described in our [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners)
 * Practical AI applications built using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-57639-dmitryso)**. For this, we recommend that you start with modules Microsoft Learn for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-57639-dmitryso), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso) and others.
-* Specific ML **Cloud Frameworks**, such as [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-57639-dmitryso) or [Azure Databricks](). Consider using [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-57639-dmitryso) and [Build and O perate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-57639-dmitryso) learning paths.
+* Specific ML **Cloud Frameworks**, such as [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-57639-dmitryso) or [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-57639-dmitryso). Consider using [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-57639-dmitryso) and [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-57639-dmitryso) learning paths.
 * **Conversational AI** and **Chat Bots**. There is a separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-57639-dmitryso) learning path, and you can also refer to [this blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more detail.
 * **Deep Mathematics** behind deep learning. For this, we would recommend [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
 
@@ -40,52 +40,52 @@ For a gentle introduction to *AI in the Cloud* topics you may consider taking th
 <tr><th>No</th><th>Lesson</th><th>Intro</th><th>PyTorch</th><th>Keras/TensorFlow</th><th>Lab</th></tr>
 
 <tr><td>I</td><td colspan="4"><b>Introduction to AI</b></td><td>PAT</td></tr>
-<tr><td>1</td><td>Introduction and History of AI</td><td><a href="1-Intro/README.md">Text</a></td><td></td><td></td><td></td></tr>
+<tr><td>1</td><td>Introduction and History of AI</td><td><a href="/lessons/1-Intro/README.md">Text</a></td><td></td><td></td><td></td></tr>
 
 <tr><td>II</td><td colspan="4"><b>Symbolic AI</b></td><td>PAT</td></tr>
-<tr><td>2 </td><td>Knowledge Representation and Expert Systems</td><td><a href="2-Symbolic/README.md">Text</a></td><td colspan="2"><a href="2-Symbolic/Animals.ipynb">Expert System</a>, <a href="2-Symbolic/FamilyOntology.ipynb">Ontology</a>, <a href="2-Symbolic/MSConceptGraph.ipynb">Concept Graph</a></td><td></td></tr>
-<tr><td>III</td><td colspan="4"><b><a href="3-NeuralNetworks/README.md">Introduction to Neural Networks</a></b></td><td>PAT</td></tr>
+<tr><td>2 </td><td>Knowledge Representation and Expert Systems</td><td><a href="/lessons/2-Symbolic/README.md">Text</a></td><td colspan="2"><a href="/lessons/2-Symbolic/Animals.ipynb">Expert System</a>, <a href="/lessons/2-Symbolic/FamilyOntology.ipynb">Ontology</a>, <a href="/lessons/2-Symbolic/MSConceptGraph.ipynb">Concept Graph</a></td><td></td></tr>
+<tr><td>III</td><td colspan="4"><b><a href="/lessons/3-NeuralNetworks/README.md">Introduction to Neural Networks</a></b></td><td>PAT</td></tr>
 <tr><td>3</td><td>Perceptron</td>
-   <td><a href="3-NeuralNetworks/03-Perceptron/README.md">Text</a>
-   <td colspan="2"><a href="3-NeuralNetworks/03-Perceptron/Perceptron.ipynb">Notebook</a></td><td><a href="3-NeuralNetworks/03-Perceptron/lab/README.md">Lab</a></td></tr>
-<tr><td>4 </td><td>Multi-Layered Perceptron and Creating our own Framework</td><td><a href="3-NeuralNetworks/04-OwnFramework/README.md">Text</a></td><td colspan="2"><a href="3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb">Notebook</a><td><a href="3-NeuralNetworks/04-OwnFramework/lab/README.md">Lab</a></td></tr> 
+   <td><a href="/lessons/3-NeuralNetworks/03-Perceptron/README.md">Text</a>
+   <td colspan="2"><a href="/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb">Notebook</a></td><td><a href="/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md">Lab</a></td></tr>
+<tr><td>4 </td><td>Multi-Layered Perceptron and Creating our own Framework</td><td><a href="/lessons/3-NeuralNetworks/04-OwnFramework/README.md">Text</a></td><td colspan="2"><a href="/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb">Notebook</a><td><a href="/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md">Lab</a></td></tr> 
 <tr><td>5</td>
    <td>Intro to Frameworks (PyTorch/TensorFlow)<br/>Overfitting</td>
-   <td><a href="3-NeuralNetworks/05-Frameworks/README.md">Text</a><br/><a href="3-NeuralNetworks/05-Frameworks/Overfitting.md">Text</a></td>
-   <td><a href="3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb">PyTorch</td>
-   <td><a href="3-NeuralNetworks/05-Frameworks/IntroKerasTF.md">Keras/TensorFlow</td>
-   <td><a href="3-NeuralNetworks/05-Frameworks/lab/README.md">Lab</a></td></tr>
-<tr><td>IV</td><td colspan="2"><b><a href="4-ComputerVision/README.md">Computer Vision</a></b></td>
+   <td><a href="/lessons/3-NeuralNetworks/05-Frameworks/README.md">Text</a><br/><a href="/lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md">Text</a></td>
+   <td><a href="/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb">PyTorch</td>
+   <td><a href="/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.md">Keras/TensorFlow</td>
+   <td><a href="/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md">Lab</a></td></tr>
+<tr><td>IV</td><td colspan="2"><b><a href="/lessons/4-ComputerVision/README.md">Computer Vision</a></b></td>
   <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
   <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
   <td>PAT</td></tr>
 <tr><td>6</td><td>Intro to Computer Vision. OpenCV</td><td>Text<td colspan="2">Notebook</td><td></td></tr>
-<tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures</td><td><a href="4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td><a href="4-ComputerVision/07-ConvNets/lab/README.md">Lab</a></td></tr>
-<tr><td>8</td><td>Pre-trained Networks and Transfer Learning<br/>Training Tricks</td><td><a href="4-ComputerVision/08-TransferLearning/README.md">Text</a><br/><a href="4-ComputerVision/08-TransferLearning/TrainingTricks.md">Text</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb">TensorFlow</a><br/><a href="4-ComputerVision/08-TransferLearning/Dropout.ipynb">Dropout sample</a></td><td><a href="4-ComputerVision/08-TransferLearning/lab/README.md">Lab</a></td></tr>
-<tr><td>9</td><td>Autoencoders and VAEs</td><td><a href="4-ComputerVision/09-Autoencoders/README.md">Text</a></td><td><a href="4-ComputerVision/09-Autoencoders/AutoEncodersPytorch.ipynb">PyTorch</td><td><a href="4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb">TensorFlow</a></td><td></td></tr>
-<tr><td>10</td><td>Generative Adversarial Networks</td><td><a href="4-ComputerVision/10-GANs/README.md">Text</a></td><td><a href="4-ComputerVision/10-GANs/GANPyTorch.ipynb">PyTorch</td><td><a href="4-ComputerVision/10-GANs/GANTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures</td><td><a href="/lessons/4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td><a href="/lessons/4-ComputerVision/07-ConvNets/lab/README.md">Lab</a></td></tr>
+<tr><td>8</td><td>Pre-trained Networks and Transfer Learning<br/>Training Tricks</td><td><a href="/lessons/4-ComputerVision/08-TransferLearning/README.md">Text</a><br/><a href="/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md">Text</a></td><td><a href="/lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb">PyTorch</a></td><td><a href="/lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb">TensorFlow</a><br/><a href="/lessons/4-ComputerVision/08-TransferLearning/Dropout.ipynb">Dropout sample</a></td><td><a href="/lessons/4-ComputerVision/08-TransferLearning/lab/README.md">Lab</a></td></tr>
+<tr><td>9</td><td>Autoencoders and VAEs</td><td><a href="/lessons/4-ComputerVision/09-Autoencoders/README.md">Text</a></td><td><a href="/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPytorch.ipynb">PyTorch</td><td><a href="/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>10</td><td>Generative Adversarial Networks</td><td><a href="/lessons/4-ComputerVision/10-GANs/README.md">Text</a></td><td><a href="/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb">PyTorch</td><td><a href="/lessons/4-ComputerVision/10-GANs/GANTF.ipynb">TensorFlow</a></td><td></td></tr>
 <tr><td>11</td><td>Object Detection</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
-<tr><td>12</td><td>Semantic Segmentation. U-Net</td><td><a href="4-ComputerVision/12-Segmentation/README.md">Text</a></td><td><a href="4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb">PyTorch</td><td><a href="4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb">TensorFlow</td><td></td></tr>
-<tr><td>V</td><td colspan="2"><b><a href="5-NLP/README.md">Natural Language Processing</a></b></td>
+<tr><td>12</td><td>Semantic Segmentation. U-Net</td><td><a href="/lessons/4-ComputerVision/12-Segmentation/README.md">Text</a></td><td><a href="/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb">PyTorch</td><td><a href="/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb">TensorFlow</td><td></td></tr>
+<tr><td>V</td><td colspan="2"><b><a href="/lessons/5-NLP/README.md">Natural Language Processing</a></b></td>
    <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
    <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
    <td>PAT</td></tr>
-<tr><td>13</td><td>Text Representation. Bow/TF-IDF</td><td><a href="5-NLP/13-TextRep/README.md">Text</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationTF.ipynb">TensorFlow</td><td></td></tr>
-<tr><td>14</td><td>Semantic word embeddings. Word2Vec and GloVe</td><td><a href="5-NLP/14-Embeddings/README.md">Text</td><td><a href="5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/14-Embeddings/EmbeddingsTF.ipynb">TensorFlow</a></td><td></td></tr>
-<tr><td>15</td><td>Language Modeling. Training your own embeddings</td><td><a href="5-NLP/15-LanguageModeling">Text</a></td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
-<tr><td>16</td><td>Recurrent Neural Networks</td><td><a href="5-NLP/16-RNN/README.md">Text</a></td><td><a href="5-NLP/16-RNN/RNNPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/16-RNN/RNNTF.ipynb">TensorFlow</a></td><td></td></tr>
-<tr><td>17</td><td>Generative Recurrent Networks</td><td><a href="5-NLP/17-GenerativeNetworks/README.md">Text</a></td><td><a href="5-NLP/17-GenerativeNetworks/GenerativePyTorch.md">PyTorch</a></td><td><a href="5-NLP/17-GenerativeNetworks/GenerativeTF.md">TensorFlow</a></td><td></td></tr>
-<tr><td>18</td><td>Transformers. BERT.</td><td><a href="5-NLP/18-Transformers/README.md">Text</a></td><td><a href="5-NLP/18-Transformers/TransformersPyTorch.md">PyTorch</a></td><td><a href="5-NLP/18-Transformers/TransformersTF.md">TensorFlow</a></td><td></td></tr>
+<tr><td>13</td><td>Text Representation. Bow/TF-IDF</td><td><a href="/lessons/5-NLP/13-TextRep/README.md">Text</a></td><td><a href="/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb">TensorFlow</td><td></td></tr>
+<tr><td>14</td><td>Semantic word embeddings. Word2Vec and GloVe</td><td><a href="/lessons/5-NLP/14-Embeddings/README.md">Text</td><td><a href="/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>15</td><td>Language Modeling. Training your own embeddings</td><td><a href="/lessons/5-NLP/15-LanguageModeling">Text</a></td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
+<tr><td>16</td><td>Recurrent Neural Networks</td><td><a href="/lessons/5-NLP/16-RNN/README.md">Text</a></td><td><a href="/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb">PyTorch</a></td><td><a href="/lessons/5-NLP/16-RNN/RNNTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>17</td><td>Generative Recurrent Networks</td><td><a href="/lessons/5-NLP/17-GenerativeNetworks/README.md">Text</a></td><td><a href="/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.md">PyTorch</a></td><td><a href="/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.md">TensorFlow</a></td><td></td></tr>
+<tr><td>18</td><td>Transformers. BERT.</td><td><a href="/lessons/5-NLP/18-Transformers/README.md">Text</a></td><td><a href="/lessons/5-NLP/18-Transformers/TransformersPyTorch.md">PyTorch</a></td><td><a href="/lessons/5-NLP/18-Transformers/TransformersTF.md">TensorFlow</a></td><td></td></tr>
 <tr><td>19</td><td>Named Entity Recognition</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
 <tr><td>20</td><td>Large Language Models, Prompt Programming and Few-Shot Tasks</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
 <tr><td>VI</td><td colspan="4"><b>Other AI Techniques</b></td><td>PAT</td></tr>
-<tr><td>21</td><td>Genetic Algorithms</td><td><a href="6-Other/21-GeneticAlgorithms/README.md">Text</a><td colspan="2"><a href="6-Other/21-GeneticAlgorithms/Genetic.ipynb">Notebook</a></td><td></td></tr>
-<tr><td>22</td><td>Deep Reinforcement Learning</td><td><a href="6-Other/22-DeepRL/README.md">Text</a></td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
-<tr><td>23</td><td>Multi-Agent Systems</td><td><a href="6-Other/23-MultiagentSystems/README.md">Text</a></td><td></td><td></td><td></td></tr>
+<tr><td>21</td><td>Genetic Algorithms</td><td><a href="/lessons/6-Other/21-GeneticAlgorithms/README.md">Text</a><td colspan="2"><a href="/lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb">Notebook</a></td><td></td></tr>
+<tr><td>22</td><td>Deep Reinforcement Learning</td><td><a href="/lessons/6-Other/22-DeepRL/README.md">Text</a></td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
+<tr><td>23</td><td>Multi-Agent Systems</td><td><a href="/lessons/6-Other/23-MultiagentSystems/README.md">Text</a></td><td></td><td></td><td></td></tr>
 <tr><td>VII</td><td colspan="4"><b>AI Ethics</b></td><td>PAT</td></tr>
-<tr><td>24</td><td>AI Ethics and Responsible AI</td><td><a href="7-Ethics/README.md">Text</a></td><td></td><td></td><td></td></tr>
+<tr><td>24</td><td>AI Ethics and Responsible AI</td><td><a href="/lessons/7-Ethics/README.md">Text</a></td><td></td><td></td><td></td></tr>
 <tr><td></td><td colspan="4"><b>Extras</b></td><td></td></tr>
-<tr><td>1</td><td>Multi-Modal Networks, CLIP and VQGAN</td><td><a href="X-Extras/1-MultiModal/README.md">Text</a></td><td></td><td></td><td></td></tr>
+<tr><td>1</td><td>Multi-Modal Networks, CLIP and VQGAN</td><td><a href="/lessons/X-Extras/X1-MultiModal/README.md">Text</a></td><td></td><td></td><td></td></tr>
 </table>
 
 **[Mindmap of the Course](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)**
@@ -113,7 +113,7 @@ However, if you would like to take the course as a self-study project, we sugges
 
 > For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
 
-**Teachers**, we have [included some suggestions](etc/for-teachers.md) on how to use this curriculum.
+**Teachers**, we have [included some suggestions](/etc/for-teachers.md) on how to use this curriculum.
 
 ---
 
@@ -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](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!
 

etc/docsify-to-pdf/docs/_sidebar.md

@@ -0,0 +1,20 @@
+- Introduction
+  - [Introduction to AI](../../../lessons/1-Intro/README.md)
+  - [Knowledge Representation and Expert Systems](../../../lessons/2-Symbolic/README.md)
+  - [Neural Networks - Perceptron](../../../lessons/3-NeuralNetworks/03-Perceptron/README.md)
+  - [Neural Networks - Your Own Framework](../../../lessons/3-NeuralNetworks/04-OwnFramework/README.md)
+  - [Neural Networks - Perceptron](../../../lessons/3-NeuralNetworks/05-Frameworks/README.md)
+  - [Computer Vision - Convolutional Neural Networks](../../../lessons/4-ComputerVision/07-ConvNets/README.md)
+  - [Computer Vision - Transfer Learning](../../../lessons/4-ComputerVision/08-TransferLearning/README.md)
+  - [Computer Vision - Autoencoders](../../../lessons/4-ComputerVision/09-Autoencoders/README.md)
+  - [Computer Vision - GANs](../../../lessons/4-ComputerVision/10-GANs/README.md)
+  - [Computer Vision - Segmentation](../../../lessons/4-ComputerVision/12-Segmentation/README.md)
+  - [NLP - Text Representation](../../../lessons/5-NLP/13-TextRep/README.md)
+  - [NLP - Embeddings](../../../lessons/5-NLP/14-Embeddings/README.md)
+  - [NLP - LanguageModeling](../../../lessons/5-NLP/15-LanguageModeling/README.md)
+  - [NLP - RNNs](../../../lessons/5-NLP/16-RNN/README.md)
+  - [NLP - Generative Networks](../../../lessons/5-NLP/17-GenerativeNetworks/README.md)
+  - [NLP - Transformers](../../../lessons/5-NLP/18-Transformers/README.md)
+  - [Other - Genetic Algorithms](../../../lessons/6-Other/21-GeneticAlgorithms/README.md)
+  - [Other - Multiagent Systems](../../../lessons/6-Other/23-MultiagentSystems/README.md)
+  - [Ethics](../../../lessons/7-Ethics/README.md)

etc/docsify-to-pdf/docsifytopdf.js

@@ -1,6 +1,6 @@
 module.exports = {
-	contents: ['docs/_sidebar.md'], // array of "table of contents" files path
-	pathToPublic: 'pdf/readme.pdf', // path where pdf will stored
+	contents: ['etc/docsify-to-pdf/docs/_sidebar.md'], // array of "table of contents" files path
+	pathToPublic: 'etc/docsify-to-pdf/pdf/readme.pdf', // path where pdf will stored
 	pdfOptions: {
 		margin: { top: '100px', bottom: '100px' }
 	}, // reference: https://github.com/GoogleChrome/puppeteer/blob/master/docs/api.md#pagepdfoptions

etc/docsify/docs/_sidebar.md

@@ -1,3 +0,0 @@
-- Introduction
-  - [Defining Data Science](../1-Introduction/01-defining-data-science/README.md)
-  

etc/docsify/index.html

@@ -1,30 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-  <meta charset="UTF-8">
-  <title>AI for Beginners</title>
-  <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
-  <meta name="description" content="Description">
-  <meta name="viewport"
-    content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
-  <link rel="icon" type="image/png" href="images/favicon.png">
-  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/docsify-themeable@0/dist/css/theme-simple.css">
-</head>
-
-<body>
-  <div id="app"></div>
-  <script>
-    window.$docsify = {
-      name: 'AI for Beginners',
-      repo: 'https://github.com/Microsoft/AI-For-Beginners',
-      relativePath: false,
-      basePath: 'https://raw.githubusercontent.com/microsoft/ai-for-beginners/main/',
-      auto2top: true,
-      routerMode: 'history'
-    }
-  </script>
-  <script src="//cdn.jsdelivr.net/npm/docsify/lib/docsify.min.js"></script>
-</body>
-
-</html>

index.html

@@ -1 +1,29 @@
-<meta http-equiv="refresh" content="0; url=https://github.com/jlooper/AI-For-Beginners/tree/main/etc/docsify">
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8">
+  <title>AI for Beginners</title>
+  <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
+  <meta name="description" content="Description">
+  <meta name="viewport"
+    content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
+  <link rel="icon" type="image/png" href="images/favicon.png">
+  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/docsify-themeable@0/dist/css/theme-simple.css">
+</head>
+
+<body>
+  <div id="app"></div>
+  <script>
+    window.$docsify = {
+      name: 'AI for Beginners',
+      repo: 'https://github.com/Microsoft/AI-For-Beginners',
+      relativePath: true,
+      auto2top: true,
+      routerMode: 'history'
+    }
+  </script>
+  <script src="//cdn.jsdelivr.net/npm/docsify/lib/docsify.min.js"></script>
+</body>
+
+</html>

lessons/3-NeuralNetworks/04-OwnFramework/README.md

@@ -58,7 +58,7 @@ Note that the left-most part of all those expressions is the same, and thus we c
 
 <img alt="compute graph" src="images/ComputeGraphGrad.png"/>
 
-> Image by author
+> TODO: image citation
 
 > ✅ We will cover backprop in much more detail in our notebook example.