Part III: Deep Learning Foundations

"What I cannot create, I do not understand." — Richard Feynman

Deep learning has transformed AI from a niche academic discipline into the engine powering products used by billions of people. In Part III, you will build deep neural networks from the ground up, understand how they learn, and master the architectures that laid the groundwork for the transformer revolution.

We begin by constructing neural networks from scratch — implementing forward passes, backpropagation, and gradient updates in pure NumPy before transitioning to PyTorch. You will understand exactly what happens when you call loss.backward(). We then tackle the practical challenges of training deep networks: choosing loss functions, selecting optimizers, scheduling learning rates, and diagnosing training failures. Regularization and generalization teach you how to build models that work beyond the training set.

The second half of Part III explores the architectures that defined the deep learning era before transformers. Convolutional neural networks revolutionized computer vision. Recurrent neural networks and LSTMs enabled sequence modeling. Autoencoders opened the door to representation learning. Generative adversarial networks demonstrated that neural networks could create as well as classify. Each of these architectures introduces concepts — feature hierarchies, gating mechanisms, latent spaces, adversarial training — that reappear in modern transformer-based systems.

Chapters in This Part

Chapter Title Key Question
11 Neural Networks from Scratch How does a neural network actually learn?
12 Training Deep Networks How do we reliably train networks with millions of parameters?
13 Regularization and Generalization How do we prevent overfitting and ensure models generalize?
14 Convolutional Neural Networks How do we build networks that understand spatial structure?
15 Recurrent Neural Networks and Sequence Modeling How do we process sequential data like text and time series?
16 Autoencoders and Representation Learning How do we learn compressed, meaningful representations of data?
17 Generative Adversarial Networks How do we train networks to generate realistic data?

What You Will Be Able to Do After Part III

  • Implement neural networks from scratch and in PyTorch
  • Diagnose and fix common training problems
  • Build and train CNNs for image classification
  • Apply RNNs and LSTMs to sequence data
  • Train autoencoders for representation learning and generation
  • Understand adversarial training dynamics

Prerequisites

  • Part I (especially linear algebra and calculus)
  • Part II (model evaluation concepts)
  • Basic PyTorch familiarity (introduced in Chapter 11)

Chapters in This Part