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@@ -78,7 +78,32 @@ Instructions for editing figures / equations can be found <a href="https://drive
 
 <h2>Resources for students</h2>
 
-Answers to selected questions: <a href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf>PDF</a>
+<p>Answers to selected questions: <a href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf>PDF</a></p>
+<p>Python notebooks:</p>
+
+<ul> 
+	<li>  Chapter 1 - Introduction: <a href="">1.1</a>
+	<li>  Chapter 2 - Supervised learning <a href="">2.1</a>
+	<li>  Chapter 3 - Shallow neural networks <a href="">3.1</a>,<a href="">3.2</a>,<a href="">3.3</a>,<a href="">3.4</a>
+	<li>  Chapter 4 - Deep neural networks <a href="">4.1</a>, 4.2, 4.3 (coming soon)
+	<li>  Chapter 5 - Loss functions (coming soon)
+	<li>  Chapter 6 - Training models (coming soon)
+	<li>  Chapter 7 - Gradients and initialization (coming soon)
+	<li>  Chapter 8 - Measuring performance (coming soon)
+	<li>  Chapter 9 - Regularization (coming soon)
+	<li>  Chapter 10 - Convolutional networks (coming soon)
+	<li>  Chapter 11 - Residual networks (coming soon)
+	<li>  Chapter 12 - Transformers (coming soon)
+	<li>  Chapter 13 - Graph neural networks (coming soon)
+	<li>  Chapter 14 - Unsupervised learning (coming soon)
+	<li>  Chapter 15 - Generative adversarial networks (coming soon)
+	<li>  Chapter 16 - Normalizing flows (coming soon)
+	<li>  Chapter 17 - Variational autoencoders (coming soon)
+	<li>  Chapter 18 - Diffusion models (coming soon)
+	<li>  Chapter 19 - Deep reinforcement learning (coming soon)
+	<li>  Chapter 20 - Why does deep learning work? (coming soon)
+	<li>  Chapter 21 - Deep learning and ethics (coming soon)
+</ul>
 
 <br>
 <h2>Citation:</h2>