Neural Networks and Deep Learning

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

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