When you are comfortable with the prerequisites, we suggest four options for studying deep learning. Choose any of them or any combination of them. The number of stars indicates the difficulty.
- Hugo Larochelle's video course on YouTube. The videos were recorded in 2013 but most of the content is still fresh. The mathematics behind neural networks is explained in detail. Slides and related materials are available. ★★
- Stanford's CS231n (Convolutional Neural Networks for Visual Recognition) by Fei-Fei Li, Andrej Karpathy and Justin Johnson. The course is focused on image processing, but covers most of the important concepts in deep learning. Videos (2016) and lecture notes are available. ★★
- Michael Nielsen's online book Neural networks and deep learning is the easiest way to study neural networks. It doesn't cover all important topics, but contains intuitive explanations and code for the basic concepts. ★
- Deep learning, a book by Ian Goodfellow, Yoshua Bengio and Aaron Courville, is the most comprehensive resource for studying deep learning. It covers a lot more than all the other courses combined. ★★★
There are many software frameworks that provide necessary functions, classes and modules for machine learning and for deep learning in particular. We suggest you not use these frameworks at the early stages of studying, instead we suggest you implement the basic algorithms from scratch. Most of the courses describe the maths behind the algorithms in enough detail, so they can be easily implemented.
- Jupyter notebooks are a convenient way to play with Python code. They are nicely integrated with matplotlib, a popular tool for visualizations. We suggest you implement algorithms in such environments. ★