Module 00: ML & PyTorch Foundations

Chapter Overview

This chapter is your launchpad. Before we can understand how Large Language Models work, we need to build a solid foundation in machine learning, neural networks, and the tools we will use throughout the course. Think of this chapter as ensuring everyone speaks the same language before the real journey begins.

We start with the core ideas of machine learning: how machines learn patterns from data, what can go wrong (overfitting), and how to fix it. Then we dive into neural networks and the magic of backpropagation. Next, we get our hands dirty with PyTorch, the framework that powers most modern LLM research and development. Finally, we introduce reinforcement learning, the paradigm that makes LLMs helpful through RLHF.

Learning Objectives

Explain supervised learning, loss functions, and gradient descent intuitively and mathematically
Describe the bias-variance tradeoff and apply regularization techniques
Build and train neural networks, understanding backpropagation at a mechanical level
Write complete PyTorch training loops with custom datasets and GPU acceleration
Explain the RL framework (agent, policy, reward) and its connection to LLM training

Sections

Prerequisites

Python proficiency (functions, classes, list comprehensions, decorators)
Basic linear algebra: vectors, matrices, dot products
Basic probability: distributions, expectation, Bayes' theorem
No prior ML experience required