Prerequisites
This book teaches the robotics-specific parts of physical AI. General math, machine learning, deep learning, reinforcement-learning basics, transformers and diffusion are not re-taught here: each part links to the matching lessons of the free AI Engineering from Scratch course. You need Python and the items listed for the part you are starting.
- Probability and Distributions
- Bayes’ Theorem
- Stochastic Processes
- Image Fundamentals — Pixels, Channels, Color Spaces
- Object Detection — YOLO from Scratch
- Open-Vocabulary Vision — CLIP
- SAM 3 & Open-Vocabulary Segmentation
- Monocular Depth & Geometry Estimation
- Statistics for Machine Learning
- Model Evaluation
- Multi-Layer Networks and Forward Pass
- Backpropagation from Scratch
- Loss Functions
- Introduction to PyTorch
- The Full Transformer — Encoder + Decoder
- Autoencoders & Variational Autoencoders (VAE)
- Diffusion Models — DDPM from Scratch
- Flow Matching & Rectified Flows