"Every expert was once a beginner who refused to skip the fundamentals."
Tensor, Fundamentals-Obsessed AI Agent
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 book. Think of this chapter as ensuring everyone speaks the same language before the real journey begins, from NLP fundamentals (Chapter 1) all the way through to building AI agents (Chapter 22).
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, concepts you will see again when we study the Transformer architecture (Chapter 4). 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, a topic explored in full in Chapter 17: Alignment, RLHF & DPO.
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
Learning Objectives
- Explain supervised learning, loss functions, and gradient descent intuitively and mathematically (these resurface in Chapter 14: Fine-Tuning Fundamentals)
- 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, skills applied throughout Part 4: Training & Adapting
- Explain the RL framework (agent, policy, reward) and its connection to LLM training via RLHF and DPO (Chapter 17)
Sections
- 0.1 ML Basics: Features, Optimization & Generalization 🟢 📐 Supervised learning, loss functions, gradient descent, overfitting, regularization, bias-variance tradeoff, cross-validation. These optimization concepts carry directly into pretraining and scaling (Chapter 6).
- 0.2 Deep Learning Essentials 🟢 📐 Perceptrons, MLPs, activation functions, backpropagation, batch normalization, dropout, CNNs, training best practices. The foundation for understanding sequence models and attention (Chapter 3).
- 0.3 PyTorch Tutorial 🟢 ⚙️ 🔧 Tensors, autograd, nn.Module, DataLoader, training loops, saving/loading models, debugging. Includes hands-on lab: build an image classifier. You will use these PyTorch skills in every hands-on module, especially PEFT (Chapter 15) and inference optimization (Chapter 9).
- 0.4 Reinforcement Learning Foundations 🟢 📐 Agent-environment loop, policy, value functions, Bellman equation, policy gradients, PPO, and how RL connects to LLM training (RLHF). See also AI Agents (Chapter 22) for how RL-trained policies power autonomous systems.
What's Next?
In the next section, Section 0.1: ML Basics: Features, Optimization & Generalization, we begin with the core machine learning concepts (features, optimization, and generalization) that underpin every large language model.