The Little Book of Deep Learning

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

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

Collection of open access research on deep learning methods and applications. Covers CNN architectures, medical imaging, plant disease detection, underwater robotics, and reinforcement learning.

Zoran Gacovski