惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

P
Proofpoint News Feed
博客园_首页
WordPress大学
WordPress大学
大猫的无限游戏
大猫的无限游戏
有赞技术团队
有赞技术团队
阮一峰的网络日志
阮一峰的网络日志
Hugging Face - Blog
Hugging Face - Blog
博客园 - 【当耐特】
酷 壳 – CoolShell
酷 壳 – CoolShell
Y
Y Combinator Blog
Vercel News
Vercel News
The GitHub Blog
The GitHub Blog
T
The Blog of Author Tim Ferriss
云风的 BLOG
云风的 BLOG
博客园 - 司徒正美
Engineering at Meta
Engineering at Meta
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
J
Java Code Geeks
Stack Overflow Blog
Stack Overflow Blog
N
Netflix TechBlog - Medium
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
G
Google Developers Blog
Last Week in AI
Last Week in AI

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
I wrote a free, open-source book on the math behind AI in...
Tiago Montei · 2026-05-19 · via DEV Community
Cover image for I wrote a free, open-source book on the math behind AI in freeCodeCamp

Tiago Monteiro

Why do a lot of people fail at AI?

Because the math scares them.

TLDR

Why I wrote this book?

I started learning machine learning before ChatGPT came out

A big problem I had back then was understanding the mathematics behind machine learning.

An engineering bachelor's degree later (electrical and computer engineering) and hundreds of thousands of views on my freeCodeCamp blog, I decided to write a book on the math behind AI!

Also, I am currently doing a master degree in AI at northeastern university.

The book teaches the math in simple and plain English with a lot of analogies.

Currently it has +50K+ views, and the GitHub repo has +140 stars.

Also, I tried to balance the best way possible the depth and making it accessible.

Finally, I show a lot of examples where the math concepts of AI are applied in fields of engineering and how these math ideas power billion dollar industries.

What is in the book?

  • The Architecture of Mathematics: How math connects from foundations to AI, including Gödel's paradoxes and Einstein's relativity
  • The Field of Artificial Intelligence: From Control Theory to modern AI, understanding symbolic vs. non-symbolic AI approaches
  • Linear Algebra: Vectors, matrices, determinants, eigenvalues, and transformations that show geometry of data in machine learning
  • Multivariable Calculus: Limits, Derivatives, and Integrals.
  • Probability & Statistics: Bayesian methods, distributions, and Markov models for learning from uncertainty
  • Optimization Theory: Gradient descent, Adam optimizer, and how machines learn step by step
  • Real-World Applications: A lot of Python code examples, animated visualizations, and practical examples of where the math is applied

How can you help?

Link to the book: https://www.freecodecamp.org/news/the-math-behind-artificial-intelligence-book/

The book repo is here: https://github.com/tiagomonteiro0715/The-Math-Behind-Artificial-Intelligence-A-Guide-to-AI-Foundations

If it's useful to you:

  • A star helps more people find it
  • Suggest topics you'd want covered next in a new version of the book!

Saw a mistake in the book?

Send me an email!

monteiro.t@northeastern.edu