Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Presents a comprehensive treatment of deep learning, covering the mathematical foundations of gradient descent and its application in updating neural network parameters.
Neural Networks and Deep Learning, Michael A. Nielsen, 2015 (Determination Press) - An accessible online textbook that explains backpropagation, gradient descent, and the process of updating weights and biases in neural networks.
Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 (Springer)DOI: 10.1007/978-0-387-45528-0 - A classic textbook providing a rigorous mathematical foundation for machine learning algorithms, including the principles of gradient descent for optimizing model parameters.