Pattern Recognition and Machine Learning, Christopher Bishop, 2006 (Springer) - Explains the mathematical foundations of machine learning, including linear models, loss functions, and the use of calculus for optimization.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - Provides practical examples and explanations of linear regression, cost functions, and gradient descent, connecting them to the underlying mathematical functions and optimization principles.
CS229: Machine Learning Lecture Notes - Main Notes, Andrew Ng, Tengyu Ma, 2023Stanford University CS229 Course Material (Stanford University) - Covers the mathematical foundations of linear regression, including the model as a function, cost functions, and how gradient descent uses derivatives for optimization.
Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020 (Cambridge University Press)DOI: 10.1017/9781108679904 - Provides a comprehensive overview of the mathematical concepts essential for machine learning, with dedicated sections on calculus, including functions, limits, and derivatives in the context of optimization.