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Provides 150+ executed Jupyter notebooks and code that reproduce the book 'Machine Learning for Algorithmic Trading (2nd ed.)' — covers feature engineering, alternative-data signal extraction, backtesting, NLP, deep learning and reinforcement learning for trading; best for quant researchers and practitioners.

Notebook-first deep learning textbook that teaches concepts through runnable multi-framework code, math, and exercises. Includes lecture-ready notebooks, community contributions, and broad university adoption—designed for hands-on learners and instructors.

A living, open-source textbook and curriculum for ML systems engineering — includes the textbook source, TinyTorch reference framework, hands-on labs and hardware kits, plus instructor materials for teaching training, deployment, and system-level trade-offs.

Provides runnable Colab notebooks and code that accompany an O'Reilly book, letting you learn, inspect, fine-tune, and apply large language models through chapter-based labs and ~300 custom illustrations.

Teaches the foundations and recent advances of large language models through chapter-based PDFs and curated paper lists. Monthly updates keep the syllabus current; bilingual chapter materials and a structured paper list make it suitable for courses and self-study.

Companion resources for Chip Huyen's AI Engineering book: chapter summaries, study notes, prompt examples, case studies, and a few analysis scripts. Focuses on engineering practices for adapting foundation models to production rather than step-by-step code tutorials.

An open, intuition-first textbook that teaches the maths, computing, and practical foundations needed for AI engineering. Organized into focused chapters (vectors, matrices, calculus, ML, NLP, CV, GPU/Inference, ML systems) with code-first explanations and interview-ready emphasis.

The book coveris probabilistic approaches to machine learning, including Bayesian networks, graphical models, kernel methods, and EM algorithms. It emphasizes a statistical perspective over purely algorithmic approaches, helping formalize machine learning as a probabilistic inference problem. Its clear mathematical treatment and broad coverage have made it a standard reference for researchers and graduate students. The book’s impact lies in shaping the modern probabilistic framework widely used in fields like computer vision, speech recognition, and bioinformatics, deeply influencing the development of Bayesian machine learning methods.

The book unifies key machine learning and statistical methods — from linear models and decision trees to boosting, support vector machines, and unsupervised learning. Its clear explanations, mathematical rigor, and practical examples have made it a cornerstone for researchers and practitioners alike. The book has deeply influenced both statistics and computer science, shaping how modern data science integrates theory with application, and remains a must-read reference for anyone serious about statistical learning and machine learning.

This book develops a formal theory of intelligence, defining it as an agent’s capacity to achieve goals across computable environments and grounding the concept in Kolmogorov complexity, Solomonoff induction and Hutter’s AIXI framework.It shows how these idealised constructs unify prediction, compression and reinforcement learning, yielding a universal intelligence measure while exposing the impracticality of truly optimal agents due to incomputable demands. Finally, it explores how approximate implementations could trigger an intelligence explosion and stresses the profound ethical and existential stakes posed by machines that surpass human capability.

Th book offers a comprehensive, mathematically rigorous introduction to machine learning through the lens of probability and statistics. Covering topics from Bayesian networks to graphical models and deep learning, it emphasizes probabilistic reasoning and model uncertainty. The book has become a cornerstone text in academia and industry, influencing how researchers and practitioners think about probabilistic modeling. It’s widely used in graduate courses and cited in numerous research papers, shaping a generation of machine learning experts with a solid foundation in probabilistic approaches.

The book provides a comprehensive introduction to deep learning, covering foundational concepts like neural networks, optimization, convolutional and recurrent architectures, and probabilistic approaches. It bridges theory and practice, making it essential for both researchers and practitioners. Its impact has been profound, shaping modern AI research and education, inspiring breakthroughs in computer vision, natural language processing, and reinforcement learning, and serving as the go-to reference for anyone entering the deep learning field.