The most comprehensive book on the foundational topics in deep learning. For companion videos of most of the chapters, check out this YouTube playlist.
A book that is complementary to the fast.ai courses, covering deep learning, fast.ai and lots of examples.
The content of the book is similar to the Deep Learning book but a lot more practically oriented with lots of exercises in PyTorch/TensorFlow.
A set of introductory lectures on Deep Learning by Andrew Ng with a focus on practical issues such as reading research papers, career advice and ML in healthcare.
Also a great course, but not as comprehensive as the ones in the course list. These lectures can be a great first exposure to the topics and also have great guest lectures.
The most visually intuitive introduction to neural networks, gradient descent and backpropagation.
An older course on deep learning from 2013, which covers topics that are not usually covered in more recent courses (e.g. conditional random fields, restricted Boltzman machines and sparse coding).