This hands-on course teaches learners how to build a Large Language Model (LLM) from scratch using Python and modern deep learning concepts. The training focuses on practical implementation and provides a step-by-step walkthrough of creating GPT-style language models from the ground up.
The course begins with setting up a complete AI development environment, including the tools and libraries required for machine learning and neural network experimentation.
Learners then explore how to work with text datasets, including text preprocessing, tokenization, vocabulary creation, and converting raw language into numerical representations suitable for neural network training.
A major focus of the course is understanding and coding attention mechanisms, which form the foundation of transformer architectures. Students learn how self-attention works mathematically and how it enables language models to understand relationships between words and sequences.
The course also covers the implementation of a GPT-style transformer model capable of generating text. Learners build neural network components step by step and understand how autoregressive text generation functions internally.
Advanced sections introduce pretraining techniques using unlabeled datasets, demonstrating how language models learn linguistic patterns from large-scale text corpora without manual labeling.
Throughout the course, learners gain practical experience with neural network training, model architecture design, text generation pipelines, and transformer-based AI systems.
By the end of this training, learners will understand how GPT-style large language models work internally and will have hands-on experience buil
