Pytorch For Llms: Build Gpt From Scratch
Published 7/2026
Created by Signal Labs
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 29 Lectures ( 1h 36m ) | Size: 520.5 MB
Understand how modern LLMs work by implementing every component of a GPT from scratch.

What you'll learn
⚡ Build a working GPT in PyTorch from scratch, without leaning on high-level model libraries
⚡ Understand attention deeply enough to implement it yourself
⚡ Assemble the components of a transformer into a model you can actually train
⚡ Train a language model on your own laptop, and tell whether it's genuinely learning
⚡ Generate text from your model and shape how it writes
⚡ Turn your model into a small chatbot that answers questions
⚡ Explain why each part of a transformer exists and how they fit together
⚡ Reproduce the whole build on your own machine, end to end
Requirements
❗ A computer where you can install Python is all you truly need. You don't need any machine-learning background, and you don't need to be fluent in Python - the math and the code you'll rely on are introduced from scratch, right before they're used. A little prior programming experience will help you move faster, but it isn't required.
Description
Note: This course contains the use of artificial intelligence.
PyTorch for LLMs: Build GPT from Scratch is a hands-on course that takes you from an empty file to a working language model you've written and trained yourself. It's built for developers, students, and anyone who uses AI tools and wants to understand what's actually happening inside them - no machine-learning background required.
Over the course you go from the single idea a language model rests on - predicting the next token - to a small but complete GPT, the same architecture behind today's large models, running and training on your own machine. Every part is written by hand in PyTorch, so by the end nothing about it stays a black box.
The course opens with foundations. You set up Python and PyTorch and cover the groundwork the rest of the course leans on: the vectors and matrix multiplication behind every layer, the small amount of probability and logarithms that show up in training, and a plain-language look at how a neural network actually learns. Nothing is assumed; each idea is introduced right before it's needed.
From there you build the model's front end - a character-level tokenizer that turns text into numbers, the data handling that feeds it, and a simple baseline you can train to see a loss curve move. Then comes the heart of the course: attention. You implement self-attention directly, with queries, keys, and values and the causal masking that keeps the model honest about what it can see, then extend it to multiple heads and wrap it, along with feed-forward layers, normalization, and residual connections, into transformer blocks. You stack those blocks, add positional information, and arrive at a full GPT.
With the model built, you train it - watching the loss fall as it teaches itself to write from raw text - then generate from it and see how sampling choices shape what it produces. In the final part, you reshape your data into question-and-answer form and retrain, turning the model you built into a small chatbot that responds to prompts.
Everything runs locally and is yours to keep and revisit. By the end, you won't just know that transformers work - you'll have built one, and you'll understand why each piece is there.
Who this course is for
⭐ Developers and students who use AI tools and want to understand how they actually work
⭐ Aspiring ML engineers who want first-principles intuition for attention and training before reaching for large frameworks
⭐ Anyone curious about transformers and large language models who learns best through hands-on implementation
⭐ AI enthusiasts who want a practical introduction to transformer architectures


https://rapidgator.net/file/e9b658b29e30f40c06e21023390f9a18/PyTorch_for_LLMs_Build_GPT_from_Scratch.rar.html