Foundations of Large Language Models

Large Language Models are reshaping technology, but understanding how they actually process, learn, and generate text is key to building real-world applications. This text-based course demystifies the inner workings of LLMs without requiring an advanced mathematics background.\n\nYou will transition from a curious developer to someone who understands the entire LLM pipeline, from raw text processing to optimization and local deployment.\n\nWhat you'll learn:\n- Learn the core mechanics of tokenization, embeddings, and the Transformer architecture using PyTorch\n- Understand how LLMs are pre-trained and how to evaluate their performance using standard benchmarks\n- Apply fine-tuning techniques to adapt open-source models for specific tasks and domains\n- Configure quantization and optimization strategies to run models efficiently on consumer-grade hardware\n- Explore modern integration patterns including basic Retrieval-Augmented Generation (RAG) and vector databases\n\nStarting with fundamental machine learning concepts and terminology, the course guides you through clear written explanations and practical code snippets. You will build a conceptual map of how these systems function and how to programmatically interact with them.\n\nThis course is designed for software engineers, data enthusiasts, and beginners looking for a clear, conceptual, and code-supported introduction to modern language models. No prior deep learning experience is required.\n\nStart reading today to build a solid foundation in the technology driving the AI revolution.