Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This seminal textbook offers a foundational understanding of deep learning concepts, including the mathematical and theoretical basis of various loss functions and their role in network training.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide demonstrating how to implement deep learning models using Keras, with specific examples and explanations of common loss functions for regression and classification tasks.
Keras losses API, Keras Team, 2024 - The official documentation for Keras provides comprehensive details on available loss functions, their configurations, and how to use them within the Keras framework.
Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 (Springer) - This classic textbook presents a thorough statistical and probabilistic foundation for machine learning algorithms, including the derivation and properties of various error functions.