Language Modeling with Transformers and Generative AI

Transformer models have revolutionized how machines understand and generate human language, powering today's most advanced AI systems. If you want to move beyond using pre-built APIs and truly understand how these architectures work under the hood, this course provides the clear foundation you need. You will journey from the absolute basics of natural language processing to the inner workings of state-of-the-art generative models. Through structured written explanations and step-by-step code walkthroughs, you will gain a practical working knowledge of self-attention mechanisms, encoder-decoder designs, and modern model optimization strategies. What you'll learn: - Understand the foundational mechanics of word embeddings, tokenization, and positional encoding. - Configure and train transformer models for text classification using encoder architectures like BERT. - Implement causal language modeling for text generation using decoder architectures like GPT. - Apply self-attention and multi-head attention concepts directly within your code. - Explore modern concepts in Retrieval-Augmented Generation (RAG) to connect language models with custom data. - Practice fine-tuning techniques to adapt pre-trained models for specific downstream tasks. The course begins with essential terminology and core definitions before guiding you through the step-by-step implementation of attention layers, training workflows, and model evaluation techniques. This text-only course is designed for software developers, data analysts, and tech enthusiasts who want to build a strong foundational understanding of generative AI. No prior experience with deep learning is required, though a basic familiarity with Python is helpful. Start reading today to unlock the mechanics of generative language models and begin building your own intelligent text applications.