Visual, textbook-style volumes. Each one builds on the last, taking you from a single perceptron to modern state-of-the-art architectures.
The big picture before the equations
A gentle, big-picture course for absolute beginners — starting with why AI exists at all, and building up a clear, correct mental model of how Artificial Intelligence, Machine Learning, and Deep Learning actually relate to each other.
From a single neuron to a network that learns
A story-driven, from-first-principles walk through neural networks — starting with a single artificial neuron and ending with a fully trained multi-layer perceptron, backpropagation, optimizers, and everything in between.
From pixels to predictions — how machines learn to see
A story-driven, from-first-principles walk through convolutional neural networks — starting with why plain MLPs fail on images and ending with modern architectures, training pipelines, and the interview questions that test it all.
How machines learn to remember — and forget — on purpose
A story-driven, from-first-principles walk through Long Short-Term Memory networks — starting with why sequences defeat ordinary neural networks and ending with complete, real-world projects, advanced architectures, and the interview questions that test it all.
The art and science of making models that actually work
A rigorous, story-driven journey through hyperparameter optimization — from understanding what hyperparameters are and why they matter, through the mathematics of search, all the way to AutoML, experiment tracking, and production best practices.
Learning to reason clearly about uncertainty
A story-driven, from-first-principles walk through probability theory — how to quantify uncertainty, update beliefs with evidence, and reason correctly about chance in data, models, and the real world.