Book series

Mathematics Behind AI

Mathematics Behind AI - 4 books.

All series
Book cover for Mathematics Behind AI

4 books

Mathematics Behind AI

Books in this series

Book cover for The Mathematics Behind Artificial Intelligence: An Intuitive Guide to How AI Represents Data, Learns, and Makes Decisions

Book 1

The Mathematics Behind Artificial Intelligence: An Intuitive Guide to How AI Represents Data, Learns, and Makes Decisions

Simon Calder

You can run a neural network without understanding linear algebra—until you need to choose an optimizer, diagnose a plateauing loss curve, or explain why your model recommends one product over another. That's when the mathematics behind AI becomes critical. T...

Ebook price: $3.00View details
Book cover for MATHEMATICS FOR DEEP LEARNING: From Matrix Calculus and Optimization to Statistical Learning and Neural Network Training

Book 2

MATHEMATICS FOR DEEP LEARNING: From Matrix Calculus and Optimization to Statistical Learning and Neural Network Training

Simon Calder

Why do some learning rates cause training to diverge while others converge slowly? Why does one optimizer work for image classification but fail for language models? The answers lie in the mathematics that governs every gradient update, every loss landscape,...

Ebook price: $3.00View details
Book cover for THE MATHEMATICS OF MODERN AI: From LLMs to Diffusion Models, Reinforcement Learning, and Graph Neural Networks

Book 3

THE MATHEMATICS OF MODERN AI: From LLMs to Diffusion Models, Reinforcement Learning, and Graph Neural Networks

Simon Calder

The most transformative AI breakthroughs—GPT-4, Stable Diffusion, AlphaFold—share a mathematical DNA that is rarely exposed. Behind the headlines, attention mechanisms, diffusion processes, and policy gradients all emerge from the same core operations: repres...

Ebook price: $3.00View details
Book cover for THE MATHEMATICS OF TRANSFORMERS AND LLMs: Attention, Token Probabilities, Scaling Laws, and Language Model Inference

Book 4

THE MATHEMATICS OF TRANSFORMERS AND LLMs: Attention, Token Probabilities, Scaling Laws, and Language Model Inference

Simon Calder

Ask a large language model to explain the theory of relativity, and it will generate a paragraph that reads like it understands physics. Yet every word it produces is the result of multiplying matrices and applying softmax—operations that are purely mechanica...

Ebook price: $3.00View details