The era of the API wrapper is over. The era of the AI Engineer has begun.
Most developers are stuck in the “Prompt Engineering” trap. They write scripts that send data to a Black Box and hope for the best. They are passengers in the AI revolution, not drivers. This book changes that.
Large Language Models Crash Course is a hands-on, line-by-line masterclass in the physics of Artificial Intelligence. We strip away the marketing hype, open the hood, and build a Generative Pre-trained Transformer (GPT) from absolute scratch using Python and PyTorch.
You will not just learn how it works—you will build the engine that makes it work.
The engineering portfolio: By the end of this book, you will have architected the following system:
| The phase | The component | What you will build |
|---|---|---|
| Interface | The Tokenizer | Build a custom Byte-Pair Encoder (BPE) to translate English to Integers |
| Body | The Architecture | Manually stack the 12-Layer Transformer, Self-Attention, and LayerNorm |
| Mind | Pre-Training | Implement the Training Loop to minimize Cross-Entropy Loss |
| Voice | The Generator | Code the Temperature & Top-K Sampling logic to control creativity |
| Specialist | Fine-Tuning | Swap the model head to build a military-grade Spam Detector |
| Alignment | Instruct-Tuning | Domesticate the model using the Alpaca Dataset to follow commands |
| Finale | S&V CORTEX | Build a Full-Stack GUI App to chat with PDF documents locally |
Instant access to code: Download all codes, including the finale application!
Stop calling OpenAI. Start building your own.
