This book is organized into six stages, with each stage peeling back another layer of AI and gradually leading to modern AI.
Stage 1 begins with the linear model, one of the fundamental building blocks of deep learning. From there, the series progresses through MLPs, CNNs, RNNs, Transformers, and beyond.
The No-Code Edition is for readers who want to follow the structure and flow of AI without writing code.
Through graphs, illustrations, quizzes, and interactive elements, you build an intuitive understanding of why deep learning models work the way they do.
Recommended for: non-developers · product managers ·
This is where those who learn AI and those who build AI part ways. This book puts you through your first test.
No prior knowledge required. Just be ready to think it through.No prior knowledge required. Just be ready to think it through.
Free Early Access for the first 100 readersRead Chapters 1–4 for free ahead of the full release.
Up to 70% off for completing the surveyReceive a discount on the full STAGE 1 release.
Do more than read. Explore, check, and ask.
The problem appears before the formula, so every idea arrives after you know why it is needed.
Each stage carries what you learned into the architecture that follows.
AI Book does not avoid equations, but it always builds intuition first with visuals and analogies. Concepts such as averages and slopes are explained again in context, so you do not need to know all the math before you begin.
You can adjust simulations inside the text, check your understanding with chapter quizzes, and ask questions about what you are reading. It is designed to be used, not just viewed.
You can read on mobile, but a desktop provides the most comfortable experience for viewing interactive graphs alongside the full text.
The series progresses from SLP to MLP, CNN, RNN, LSTM, and Transformer. Following that order helps you understand how model architectures have evolved.
The Code Edition is for readers who want to explore AI concepts through code and live results.
Run code directly in your browser to trace the model’s computations and training process.
Recommended for: developers · data analysts ·
Python learners · developers new to AI
Adjust simulations of learning rate, gradient descent, and other core concepts. Watch the loss curve change in real time and build intuition through direct experience.
Use quizzes and term tooltips in every chapter to check what you understand, not just what feels familiar.
Ask AI whenever you get stuck on a sentence. You can stay in the chapter, and every answer is grounded in the text you are reading.