Michael Nielsen’s Neural Networks and Deep Learning has quietly become one of the most respected introductions to the subject available, and having spent time with it I can see why. In a field that moves extraordinarily fast and is often covered either too superficially or too technically, Nielsen finds a level that is genuinely educational without being inaccessible.
The book starts from first principles. Nielsen assumes you have some mathematical background, broadly calculus and a degree of comfort with linear algebra, but he does not assume any prior knowledge of machine learning or neural networks. He builds the ideas up carefully, starting with the perceptron and the basic mechanics of how a neural network learns, before moving into backpropagation, regularisation and the deeper architectures that make modern deep learning possible.
What distinguishes this book from many others in the field is the quality of the explanations. Nielsen clearly thought hard about why these ideas are true, not just how they work in practice, and that comes through on every page. The mathematical reasoning is transparent and the intuitions are built up honestly rather than hand-waved away. For a student who wants to actually understand what is happening inside a neural network rather than just use one as a black box, that approach is invaluable.
The book originated as a free online resource, which says something about Nielsen’s motivations. The print version collects that material in a form that is easier to work through systematically. Either way, the content is excellent.
For any student interested in mathematics, computing or artificial intelligence, this is one of the most honest and carefully written introductions to deep learning that I have come across. I recommend it without reservation.