[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_29":-5,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5,"_12":-5},"book",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24},"currentUser","formToken","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODQyMjY","title","The Mathematics of Generative AI: From Probability to Language Models","state","published","contactAuthorMethod","email","aboutText"," Book Overview

Generative AI can often feel like magic, but behind every token prediction, attention weight, and context window lies a clean foundation of applied mathematics. The Mathematics of Generative AI: From Probability to Language Models bridges the gap between abstract equations and modern engineering, providing developers and AI practitioners with a clear, code-first roadmap.

Instead of drowning in academic proofs, this capsule translates essential mathematical concepts—probability theory, high-dimensional vector spaces, and linear transformations—directly into practical intuition for building and understanding modern Large Language Models (LLMs).

Key Takeaways & Core Topics

Probability Foundations for LLMs: Master conditional probability, joint distributions, and sampling methods (Greedy, Top-k, Top-p) that govern autoregressive token generation.
Vector Spaces & Embeddings: Understand how high-dimensional geometry represents semantic meaning, distance metrics (Cosine, Euclidean), and matrix factorization.
The Linear Algebra of Attention: Deconstruct scaled dot-product attention, Query-Key-Value (Q, K, V) projections, and positional encodings step-by-step.
From Equations to Python: Connect mathematical formulations directly to idiomatic Python code, showing how theory translates to execution.

Target Audience

Software & AI Engineers who want to move beyond viewing LLMs as black-box APIs.
Data Science & ML Students seeking an intuitive, applied bridge between university math and state-of-the-art Generative AI architectures.
Technical Authors & Educators looking for a structured reference to explain complex AI mechanics clearly.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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