Books

Mathematical Foundations

Linear Algebra

1. "Linear Algebra Done Right" by Sheldon Axler

   • Perfect starting point

   • Clear explanations of vector spaces

   • Focuses on understanding over computation

2. "Linear Algebra and Its Applications" by Gilbert Strang

   • MIT professor's classic text

   • Excellent for practical applications

   • Includes numerical methods

3. "Introduction to Linear Algebra for Science and Engineering" by Daniel Norman

   • Applications focused

   • Great for ML context

   • Includes practice problems

Calculus & Optimization

1. "Calculus" by James Stewart

   • Comprehensive coverage

   • Clear visualizations

   • Excellent for self-study

2. "Optimization Methods in Machine Learning" by Georgios B. Giannakis

   • Focused on ML applications

   • Covers gradient descent thoroughly

   • Modern optimization techniques

Statistics & Probability

1. "Introduction to Probability" by Joseph K. Blitzstein

   • Harvard's probability course textbook

   • Excellent for ML foundations

   • Includes programming examples

2. "Statistical Inference" by Casella and Berger

   • Deep dive into statistics

   • Crucial for understanding model evaluation

   • Comprehensive reference

Machine Learning Foundations

General ML

1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

   • Free

   • The "bible" of deep learning

   • Comprehensive theoretical foundation

   • Essential reference material

2. "Pattern Recognition and Machine Learning" by Christopher Bishop

   • Classic ML text

   • Strong mathematical foundation

   • Covers key algorithms

3. "Machine Learning: A Probabilistic Perspective" by Kevin Murphy

   • Comprehensive coverage

   • Modern approaches

   • Strong theoretical backing

Neural Networks

1. "Neural Networks and Deep Learning" by Michael Nielsen

   • Free online book

   • Excellent visualizations

   • Clear explanations

2. "Grokking Deep Learning" by Andrew Trask

   • Builds intuition

   • Code-first approach

   • Great for beginners

Natural Language Processing

NLP Foundations

1. "Speech and Language Processing" by Jurafsky and Martin

   • Free

   • Comprehensive NLP coverage

   • Updated for modern approaches

   • Industry standard reference

2. "Natural Language Processing with Transformers" by Lewis Tunstall et al.

   • Practical implementation focus

   • Uses Hugging Face

   • Modern architectures

3. "Foundations of Statistical Natural Language Processing" by Manning and Schütze

   • Statistical fundamentals

   • Classical approaches

   • Essential background

GenAI Specific

Language Models

1. "Transformers for Natural Language Processing" by Denis Rothman

   • Focused on transformer architecture

   • Practical examples

   • Python implementation

Production & Systems

1. "Designing Machine Learning Systems" by Chip Huyen

   • Production focus

   • Real-world applications

   • System design principles

2. "Machine Learning Engineering" by Andriy Burkov

   • Production deployment

   • Best practices

   • System architecture

Programming & Implementation

Python

1. "Fluent Python" by Luciano Ramalho

   • Advanced Python concepts

   • Essential for ML development

   • Performance optimization

2. "High Performance Python" by Micha Gorelick

   • Optimization techniques

   • Parallel processing

   • Essential for large models

PyTorch

1. "Deep Learning with PyTorch" by Eli Stevens

   • Official PyTorch book

   • Practical examples

   • Modern approaches

2. "Programming PyTorch for Deep Learning" by Ian Pointer

   • Creating custom models

   • Performance optimization

   • Production deployment

Visualization & Communication

1. "Storytelling with Data" by Cole Nussbaumer Knaflic

   • Data visualization

   • Presenting results

   • Communication skills

2. "Good Charts" by Scott Berinato

   • Chart design

   • Visual communication

   • Technical presentation

Supplementary Skills

Distributed Systems

1. "Designing Data-Intensive Applications" by Martin Kleppmann

   • Distributed systems

   • Scaling considerations

   • Essential for large models

Ethics & Safety

1. "AI Ethics" by Mark Coeckelbergh

   • Ethical considerations

   • Safety frameworks

   • Responsible AI development