ML Learning Resources
Explore our curated collection of high-quality learning resources for machine learning
Books
20
resources
Courses
12
resources
Articles
1
resources
Guides
4
resources
Practical guide to integrating large language models into applications. Covers prompt engineering, fine-tuning, content generation, retrieval augmentation, and evaluation.
Comprehensive textbook by Richard Szeliski covering both classical and deep learning-based computer vision techniques. Includes topics on feature detection, segmentation, stereo vision, and more.
Comprehensive textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville that covers deep learning from basics to advanced concepts. Considered the definitive resource for deep learning theory.
Practical guide to deep reinforcement learning with Python and PyTorch. Covers DQN, policy gradients, actor-critic methods, and advanced topics with implementation examples.
Book by Chip Huyen covering the entire ML lifecycle from requirement gathering to monitoring. Provides practical advice on data management, feature engineering, and system architecture.
Guide to designing complex prompt pipelines for sophisticated LLM applications. Explores chaining prompts together, tool use, error handling, and evaluation methodologies.
Interactive deep learning book with code, math, and discussions. Features executable code in multiple frameworks including PyTorch, TensorFlow, and MXNet. Regularly updated with new content.
A comprehensive textbook by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Covers a wide range of machine learning concepts with mathematical rigor, including decision trees, neural networks, and support vector machines.
Free online textbook by Solon Barocas, Moritz Hardt, and Arvind Narayanan. Covers fairness in machine learning, including formal definitions, measurement, and mitigation strategies.
Practical guide by Aurélien Géron that covers ML concepts and implementation using popular Python libraries. Excellent for beginners who want to apply ML techniques with code examples.
Book by Valliappa Lakshmanan, Sara Robinson, and Michael Munn that presents reusable solutions to common ML challenges. Covers patterns for data preparation, model building, and serving solutions.
Book by Andrew Ng that focuses on the strategy of applying machine learning effectively. Covers problem framing, training set construction, error analysis, and other practical aspects of ML engineering.
Practical guide for implementing NLP models using PyTorch. Covers word embeddings, sequence models, transformers, and applications like sentiment analysis, text classification, and language generation.
A comprehensive free online book on neural networks and deep learning by Michael Nielsen. Covers fundamentals and advanced topics with clear explanations.
Practical guide to creating effective prompts for ChatGPT and other LLMs. Covers prompt patterns, use cases, and techniques for improving model outputs.
Practical guide for implementing computer vision techniques using PyTorch. Includes code for image classification, object detection, segmentation, and generative models.
Definitive textbook on reinforcement learning by Richard S. Sutton and Andrew G. Barto. Covers fundamentals, dynamic programming, Monte Carlo methods, temporal-difference learning, and policy gradient methods.
Comprehensive textbook by Dan Jurafsky and James H. Martin covering all aspects of NLP from traditional methods to deep learning approaches. Regularly updated with new content.
Book by Brian Christian exploring how machine learning systems can reflect and amplify human values. Discusses challenges in aligning AI systems with human intentions and ethics.