Understanding Transformers from Scratch: A Visual, Hands-On Journey from Neural Networks to Transformers and GPT
English | October 14, 2026 | ASIN: B0HN1LPXBL | 120 Pages | Epub | 8.91 MB
What if you could truly understand how ChatGPT-style models work, not just use them?Most explanations of Transformers stop at a diagram or jump straight into code you can't follow. Understanding Transformers from Scratch does something different. It starts with a simple guessing game, predicting the next word, and builds all the way up to a working GPT, one clear step at a time.Learn the ideas. Follow the math. Build the model.Every chapter follows the same three-part rhythm:Understand the idea. Plain-English intuition, visual explanations and relatable analogies come first, so you know why each piece exists before you see how it works.Follow the math, step by step. No hand-waving and no "it can be shown that." Every equation is derived on the page and worked through with small numerical examples, so you can check each calculation yourself.Build it in code. Each chapter ends with a hands-on PyTorch tutorial where you turn what you just learned into running code.Your journey, from zero to GPT:Counting words: unigram, bigram and trigram models, and why simple counting hits a wall.Neural networks for Language: how a model learns instead of counting, and what the loss really measures.Tokens and embeddings: how words become vectors that capture meaning.RNNs and LSTMs: sequential memory, the vanishing gradient problem and gates, with complete numerical walkthroughs.The attention revolution: self-attention, multi-head attention, causal masking, residual connections, layer normalization and positional encodings, then assembled into a full Transformer block.BERT and GPT-2: how encoder-only and decoder-only models differ, including counting every parameter in BERT-base and GPT-2 so nothing stays a black box.Building GPT from scratch in PyTorch: from tokenization and batching to attention, training and text generation, finishing with a tensor-shape map and a cheat sheet.Practice that sticks. "Try This" exercises close each chapter, and fully worked solutions in the appendix let you check your understanding.This book is for you if:You use language models every day and want to know what is really happening inside them.You are a student, developer or data professional preparing for ML interviews or moving into AI.You have tried papers and tutorials that skipped the "why" and want an explanation that connects every step.You know basic Python and are comfortable with high-school-level math. Everything else is built from the ground up.By the final page, you won't just recognize the words attention, embedding and Transformer. You will have derived the math behind them and built a working GPT with your own hands.Learn the ideas. Follow the math. Build the model. Start your journey today.
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