Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook on deep learning, providing a detailed academic treatment of neural network architectures, training algorithms like backpropagation, and optimization methods.
CS231n: Convolutional Neural Networks for Visual Recognition, Lecture Notes, Fei-Fei Li, Justin Johnson, and Serena Yeung, 2024Stanford University (Stanford University) - Provides clear and detailed explanations of the core components of neural network training, including forward and backward propagation, loss functions, and optimization, presented pedagogically for students.
Neural Networks and Deep Learning, Michael Nielsen, 2015 (Determination Press) - An accessible online book that builds neural network concepts from first principles, offering intuitive explanations of backpropagation and the iterative learning process.