Deep Learning in Neural Networks: An Overview

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

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