Resources

Books and tutorials

• Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville. The definitive reference if you already know a bit.
• Neural Networks and Deep Learning by Michael Nielsen. More tutorial style, some very good explanations, i.e. the chapter about universal approximators.
• Deep Learning with Python by Francois Chollet, author of Keras. Excellent down-to-earth practical introduction, with many advanced examples.
• Deep Learning with PyTorch
• Machine Learning Yearning by Andrew Ng. Filled with practical considerations how to scale up your deep learning project.
• Reinforcement Learning: An introduction by Sutton and Barto.
• The story about why we need to know derivatives in this course.
• Matrix Calculus Survival Guide by Anti Ingel.