Deep Learning Algorithms

Free online textbook explaining the core ideas behind neural networks and deep learning. Covers perceptrons, backpropagation, gradient descent, regularization, and convolutional networks with hands-on code examples.

Michael Nielsen

Concise introduction to deep learning covering foundations, model components, architectures (CNNs, Transformers), and applications in image generation, text synthesis, and reinforcement learning.

François Fleuret

Authoritative historical survey of deep learning with 888 references. Covers supervised, unsupervised, and reinforcement learning approaches in deep neural networks from their origins to 2014.

Jürgen Schmidhuber