- Understanding Deep Learning (Simon J.D. Prince) (2024)
- The Little Book of Deep Learning (François Fleuret) (2024)
- The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks (2022)
- Generative AI in Higher Education: The ChatGPT Effect (Cecilia Ka Yuk Chan, et al.) (2024)
- The Shallow and the Deep: A Biased Introduction to Neural Networks and Old School Machine Learning (2023)
- Machine Learning with Python Tutorial (Bernd Klein) (2022)
- Mathematical Analysis of Machine Learning Algorithms (Tong Zhang) (2023)
- Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks (2023)
- Neural Network Learning: Theoretical Foundations
- Approaching (Almost) Any Machine Learning Problem
- Foundations of Machine Learning (Mehryar Mohri, et al)
- Machine Learning Yearning (Andrew Ng)
- Machine Learning Algorithms (Jason Brownlee)
- Dive into Deep Learning (Aston Zhang, et al.)
- Reinforcement Learning: An Introduction, Second Edition
- Distributional Reinforcement Learning (Marc G. Bellemare, et al)
- Algorithms for Reinforcement Learning (Csaba Szepesvari)
- An Introduction to Deep Reinforcement Learning
- Fairness and Machine Learning: Limitations and Opportunities (2023)
- Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al) (2023)
- Understanding Machine Learning: From Theory to Algorithms
- Machine Learning from Scratch (Danny Friedman)
- Deep Learning with Python, 2nd Edition (Francois Chollet)
- Deep Learning for Coders with Fastai and PyTorch
- Deep Learning with PyTorch (Eli Stevens, et al.)
- Probabilistic Machine Learning: An Introduction (Kevin Murphy)
- Introduction to Statistical Learning: with Applications in Python (Gareth James, et al)
- Statistics and Machine Learning in Python (Edouard Duchesnay, et al.)
- Deep Learning (Ian Goodfellow, et al)
- The Amazing Journey of Reason: from DNA to Artificial Intelligence
- Machine Learning with Neural Networks (Bernhard Mehlig)
- Efficient Learning Machines: Theories, Concepts, and Applications
- Boosting: Foundations and Algorithms (Robert E. Schapire, et al)
- An Introduction to Machine Learning Interpretability
- Pen and Paper Exercises in Machine Learning (Michael Gutmann)
- Interpretable Machine Learning: Black Box Models Explainable
- Pattern Recognition and Machine Learning (Christopher Bishop)
- Gaussian Processes for Machine Learning (Carl E. Rasmussen)
- The Hundred-Page Machine Learning Book (Andriy Burkov)
- An Introduction to Statistical Learning (Gareth James, et al)
- Machine Learning Engineering (Andriy Burkov)
- Machine Learning from Scratch (Danny Friedman)
- An Introduction to Quantum Machine Learning for Engineers
- Mathematics for Machine Learning (Marc P. Deisenroth, et al.)
- Financial Machine Learning (Bryan T. Kelly, et al.) (2023)