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The complete catalog — curated by SuperML or showcased by their authors. Prefer a shorter, guided list? Try Topics or Collections.
Machine Learning › Reinforcement Learning
Reinforcement Learning, second edition
An Introduction
Machine Learning › Deep Learning
Dive Into Deep Learning
Machine Learning › ML Foundations
Mathematics for Machine Learning
Machine Learning › ML Foundations
Probabilistic Machine Learning
An Introduction
Machine Learning › ML Foundations
An Introduction to Statistical Learning
with Applications in R
Machine Learning › ML Foundations
The Elements of Statistical Learning
Data Mining, Inference, and Prediction
Machine Learning › Deep Learning
Neural Networks and Deep Learning
Software Engineering › Software Architecture
Fundamentals of Software Architecture
An Engineering Approach
Data & Infrastructure › Data Engineering
Fundamentals of Data Engineering
Plan and Build Robust Data Systems
Production AI › MLOps
Reliable Machine Learning
Production AI › AI System Design
Building Machine Learning Powered Applications
Production AI › MLOps
Introducing MLOps
LLMs & Generative AI › LLM Systems
Designing Large Language Model Applications
A Holistic Approach to LLMs
Artificial Intelligence › AI Engineering
AI Engineering
Production AI › AI System Design
Machine Learning Design Patterns
Machine Learning › Deep Learning
Generative Deep Learning
Teaching Machines to Paint, Write, Compose, and Play
Software Engineering › Software Architecture
Clean Architecture
A Craftsman's Guide to Software Structure and Design
Software Engineering › Code Quality & Craftsmanship
Effective Java
Software Engineering › Code Quality & Craftsmanship
Clean Code
A Handbook of Agile Software Craftsmanship
Production AI › MLOps
Practical MLOps
Operationalizing Machine Learning Models
Production AI › MLOps
Machine Learning Engineering with Python - Second Edition
Manage the Lifecycle of Machine Learning Models Using MLOps with Practical Examples
LLMs & Generative AI › LLM Systems
Hands-On Large Language Models
Language Understanding and Generation
LLMs & Generative AI › Transformers
Natural Language Processing with Transformers, Revised Edition
Machine Learning › Deep Learning
Deep Learning with Python
Machine Learning › ML Foundations
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow
Concepts, Tools, and Techniques to Build Intelligent Systems
Machine Learning › ML Foundations
Introduction to Machine Learning, fourth edition
Machine Learning › ML Foundations
Machine Learning, revised and updated edition
Data & Infrastructure › Distributed Systems
Designing Data-intensive Applications
The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Machine Learning › Deep Learning
Deep Learning
Pattern Recognition and Machine Learning
Artificial Intelligence
A Modern Approach
