The AI Algorithms Compendium
By Choral Software
Language: English
From uninformed search to diffusion models and large language models, The AI Algorithms Compendium derives, implements, and tests the algorithms that define artificial intelligence today.
Written for students, engineers, and self-learners who want more than a high-level overview, this 42-chapter reference pairs a rigorous theoretical treatment (theorems, proofs, and derivations where they matter) with complete, runnable Python code for every method it covers.
The book moves through six parts:
- Traditional AI: classical search, logic, and planning
- Bayesian inference and statistical machine learning: regression, trees, SVMs, clustering, dimensionality reduction
- Deep learning: CNNs, RNNs, and the Transformer architecture from first principles
- Reinforcement learning: MDPs, Q-learning, policy gradients, bandits
- Generative AI: autoencoders, GANs, diffusion models, large language models, fine-tuning, RAG, and agents
- Cross-cutting topics: interpretability, AI safety, MLOps, and ethics
Every chapter follows the same structure: motivation and intuition, the underlying mathematics, a complete working implementation, a worked example run on real or synthetic data, and exercises.
All code examples rely exclusively on public-domain and openly licensed data (MNIST, Fashion-MNIST, the UCI Machine Learning Repository, Project Gutenberg texts, and synthetic data), so every example can be run and modified freely.
Whether you are filling gaps in a computer science curriculum, preparing for technical interviews, or building a working reference library for a career in AI and machine learning, it is designed to be read cover to cover or used as a lookup reference for a specific algorithm.
Apple Books ID: 6821776466
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