Learn AI
without wasting time
Books
These are true treasures. Have them in mind when you need serious answers, because tutorials are nothing more than handbooks (and often, bad ones)
MATHEMATICS FOR MACHINE LEARNING
Every basic Math, Geometry, and Statistics knowledge you will need to deeply understand how Machine Learning works
Artificial Intelligence: A Modern Approach, Global Edition
An excellent book studying AI (not only Machine Learning). From agents to reinforcement learning, from basic classifiers to Neural Networks.
Deep Learning (Adaptive Computation and Machine Learning series)
The masterpiece for Deep Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Bayesian view of machine learning with probabilistic graphical models, approximate inference (variational Bayes, EP), and kernel methods. A classic for understanding ML as probabilistic modeling.
Transformers for Natural Language Processing
Practitioner-oriented deep dive into the transformer architecture, BERT-like encoders, GPT-style decoders, Hugging Face ecosystem, and applications such as machine translation, summarization, and GPT-3/ChatGPT-style generation.
Foundations of Large Language Models
focuses specifically on the theory and fundamentals of pre-training, generative models, prompting, and alignment.
