Chapter 1: Further Reading

Foundational Texts

Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th edition). Pearson. The definitive textbook on AI, covering the full breadth of the field from search and logic to machine learning and robotics. An essential reference for any AI engineer's bookshelf.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Available free online at deeplearningbook.org. The most comprehensive textbook on deep learning fundamentals, covering linear algebra, probability, optimization, and the major neural network architectures.
Bishop, C. M. & Bishop, H. (2024). Deep Learning: Foundations and Concepts. Springer. A modern treatment of deep learning that balances mathematical rigor with practical insight, from the author of the classic Pattern Recognition and Machine Learning.

Nilsson, N. J. (2009). The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge University Press. A thorough historical account of AI from its origins through the early 21st century, written by one of the field's pioneers.
Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux. An accessible and thoughtful overview of AI's history, current capabilities, and limitations, aimed at a broad audience.
Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. An engaging survey of the five major tribes of machine learning (symbolists, connectionists, evolutionaries, Bayesians, and analogizers) and their approaches.

Vaswani, A., et al. (2017). "Attention Is All You Need." Advances in Neural Information Processing Systems 30. The original transformer paper. Essential reading for understanding the architecture that transformed modern AI.
Devlin, J., et al. (2019). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Proceedings of NAACL-HLT 2019. Introduced the pre-train-then-fine-tune paradigm that became the standard approach for NLP tasks.
Brown, T., et al. (2020). "Language Models are Few-Shot Learners." Advances in Neural Information Processing Systems 33. The GPT-3 paper that demonstrated the remarkable few-shot capabilities of large language models and catalyzed the foundation model revolution.
Bommasani, R., et al. (2021). "On the Opportunities and Risks of Foundation Models." arXiv:2108.07258. A comprehensive Stanford report examining the implications of foundation models across technology, society, and research.

Sculley, D., et al. (2015). "Hidden Technical Debt in Machine Learning Systems." Advances in Neural Information Processing Systems 28. The influential Google paper on the engineering challenges of production ML systems. A must-read for any AI engineer.
Huyen, C. (2022). Designing Machine Learning Systems. O'Reilly Media. A practical guide to the full lifecycle of ML systems, from problem framing and data engineering through deployment and monitoring.
Burkov, A. (2020). Machine Learning Engineering. True Positive Inc. A concise, practical guide to the engineering aspects of building ML systems, covering project lifecycle, data handling, model deployment, and monitoring.
Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media. While not AI-specific, this book provides essential background on the distributed systems and data infrastructure that underpin production AI systems.

O'Neil, C. (2016). Weapons of Math Destruction. Crown. A critically important examination of how algorithmic decision-making can perpetuate inequality and cause harm, with examples from education, insurance, policing, and employment.
Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press. Available free online at fairmlbook.org. A rigorous treatment of fairness in machine learning, covering the mathematical foundations of different fairness criteria and their trade-offs.
Bender, E. M., et al. (2021). "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" Proceedings of FAccT 2021. An influential paper examining the environmental, financial, and social risks of increasingly large language models.

Andriy Burkov. (2020). "Machine Learning Engineering" blog and resources at ml-engineering.org. Practical resources on ML engineering career paths, interview preparation, and industry practices.
Stanford Institute for Human-Centered Artificial Intelligence. (2024). AI Index Report. An annual comprehensive report on the state of AI, covering research, industry, policy, and education trends. Available at aiindex.stanford.edu.

Harris, C. R., et al. (2020). "Array programming with NumPy." Nature, 585, 357--362. The definitive reference for NumPy, the foundational numerical computing library used throughout the first half of this book.
Pedregosa, F., et al. (2011). "Scikit-learn: Machine Learning in Python." Journal of Machine Learning Research, 12, 2825--2830. The reference paper for scikit-learn, the standard Python library for classical machine learning.
Paszke, A., et al. (2019). "PyTorch: An Imperative Style, High-Performance Deep Learning Library." Advances in Neural Information Processing Systems 32. The reference paper for PyTorch, the deep learning framework used in the second half of this book.

Hugging Face (huggingface.co): The hub for pre-trained models, datasets, and the Transformers library. Essential for working with foundation models.
Papers With Code (paperswithcode.com): A repository linking ML papers to their implementations and benchmark results. Invaluable for finding state-of-the-art methods for specific tasks.
Distill.pub (distill.pub): A journal featuring interactive, visual explanations of ML concepts. While no longer publishing new content, the existing articles remain outstanding learning resources.
The Batch (deeplearning.ai/the-batch): Andrew Ng's weekly newsletter covering the latest developments in AI, accessible to both beginners and practitioners.
MLOps Community (mlops.community): A community of practitioners focused on the operational aspects of ML systems, with meetups, a Slack group, and educational resources.