Awesome AI Books is a carefully organized collection of AI learning books and resources that cover fundamentals, deep learning, NLP, and computer vision. It provides a structured learning path and high-quality materials for learners at different levels.
Resource categories
Fundamentals
- Artificial Intelligence: Modern Approaches
- Machine Learning (Zhou Zhihua)
- Statistical Learning Methods (Li Hang)
- Python Machine Learning Basics
Deep Learning
- Deep Learning (Ian Goodfellow)
- Practical Deep Learning Handbooks
- Deep Learning with Python and Practical Examples
- Neural Networks and Deep Learning
Natural Language Processing
- Natural Language Processing: A Comprehensive Guide
- Statistical NLP
- Deep Learning for NLP
- Transformers for NLP
Computer Vision
- Computer Vision: Algorithms and Applications
- Deep Learning for Computer Vision
- OpenCV Programming Guide
Suggested learning path
Beginner
- Math fundamentals — linear algebra, probability, calculus
- Programming basics — Python, NumPy, Pandas
- Machine learning fundamentals — supervised/unsupervised learning
- Small practical projects
Intermediate
- Deep learning theory
- Framework practice — TensorFlow, PyTorch
- Specializations — NLP, CV, RL
- State-of-the-art techniques — Transformers, GANs
Advanced
- Model optimization
- Distributed training
- System deployment and scaling
Resource traits
- Systematic coverage from fundamentals to advanced topics
- Practical recommendations combining theory and practice
- Regular updates and curated high-quality materials
- Multilingual materials including Chinese and English resources