A structured, comprehensive glossary style reference to the algorithms powering modern AI—from foundational theory to applied machine learning, deep learning, reinforcement learning, and beyond.

Artificial Intelligence has reached every major industry—but the underlying algorithms remain scattered across papers, courses, and frameworks. This book brings them together in one clear, navigable volume.

Whether you're building systems, analyzing architectures, or just trying to keep up with the pace of innovation, this reference equips you with the full landscape of algorithmic approaches—organized, explained, and ready for use.

🧠 Inside, You’ll Discover:

📘 12 fully-loaded chapters covering every major AI domain
🧮 Foundations: Linear Algebra, Probability, Information Theory
🤖 ML & DL: Regression, SVMs, CNNs, Transformers, VAEs
🎯 RL: Q-Learning, PPO, AlphaZero, Multi-agent Systems
🧠 NLP: Word Embeddings, LLMs, NMT, Chatbot Design
👁 Vision: YOLO, U-Net, Diffusion, SLAM, 3D Reconstruction
🎤 Audio AI: Whisper, WaveNet, MuseNet
🔗 Graph & Symbolic AI: GNNs, TransE, Hybrid Systems
🔧 Metaheuristics & NAS: DARTS, Optuna, AutoML
🚗 Robotics: Neural Motion Planning, Sensor Fusion
💼 Real-World: Finance, Healthcare, Cybersecurity
💻 Frameworks: TensorFlow, PyTorch, XGBoost, Gym

🛠 Who This Book is For:

🔹 AI students who want to accelerate from confusion to clarity
🔹 Researchers who need a go-to arsenal of the entire algorithm landscape
🔹 Engineers & data scientists bridging theory with application
🔹 Tech professionals & founders aiming to decode how real AI systems work
🔹 Investors & strategists navigating the fast-evolving AI ecosystem

What Makes This Book Different

Broad coverage with clean structure
Each algo is covered in clean 800-word modules — from GANs to GNNs to A* to SVMs. It’s not a deep dive — it’s a broad, structured map of the full AI algorithmic landscape.
Perfect for orientation, comparison, and code inspiration.
From concept to implementation.
Understand what each algorithm does, why it matters, and how it’s used — then see it in clean, modern code. Python & JAVA samples included for each algorithm section.
Complete and current
Includes emerging topics like contrastive learning, graph neural networks, diffusion models, and neuro-symbolic AI.
Industry-ready insights
Apply what you learn to healthcare, finance, robotics, NLP, computer vision, and security applications.

📘 Large format – ideal for study, reference, and technical review
🎓 Written for serious learners, educators, and practitioners

Ready to master the algorithmic core of artificial intelligence?
Scroll up and get your copy today.

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Articles li�s � Artificial Intelligence Algorithms: Foundations, Application...

Artificial Intelligence Algorithms: Foundations, Applications, and Future Directions

Thomas, Gareth Morgan

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