Module 5: Deep Learning for Images

What you’ll learn in this module

This module takes you from pixels to state-of-the-art vision models.

You’ll learn:

  • What images really are as data structures and why spatial structure matters.
  • How the deep learning revolution shifted computer vision from hand-crafted to learned features.
  • Practical skills for using CNNs: building blocks, pre-trained models, and transfer learning.
  • The innovation timeline from VGG to Vision Transformers and the architectural insights that made them possible.

The Journey

Let’s talk about where this module takes you. We begin with the fundamentals and build up to cutting-edge architectures. Each part answers a crucial question.

Part 1: Understanding Images

Before we can process images with neural networks, we need to understand what images are. How do computers represent visual information, and why does spatial structure matter? We answer these questions by examining images as multidimensional arrays.

Part 2: The Deep Learning Revolution

Computer vision didn’t always work this way. Shift your attention to the historical moment when neural networks transformed the field, contrasting the old paradigm of hand-crafted features with learned representations as we follow the path from LeNet to AlexNet’s breakthrough.

Part 3: Becoming a Practitioner

Now you’ll learn the skills to actually use these models, covering CNN building blocks like convolution and pooling. You’ll work with pre-trained models, master transfer learning, and gain hands-on implementation experience.

Part 4: The Innovation Timeline

The very best way to understand modern architectures is to see them as solutions to specific problems. Why did networks need to get deeper, and how did researchers overcome training difficulties? We trace the quest for better networks through VGG, Inception, ResNet, and Vision Transformers.

Why This Matters

Here’s something remarkable: computer vision is no longer about manually designing features, as modern systems learn representations automatically from data. This shift changed everything about how we build vision applications, with what used to require expert knowledge and careful tuning now happening through learning. This module gives you both conceptual understanding and practical skills so you’ll know why architectures evolved the way they did and be able to use state-of-the-art vision models in your own projects.

Prerequisites

You should be comfortable with basic Python programming and NumPy arrays, plus neural network fundamentals like forward propagation, backpropagation, and gradient descent. You’ll also need PyTorch basics like tensors, autograd, and simple model training (review the earlier modules in this course if you need to refresh these topics).

What You’ll Build

By the end of this module, you will understand how images are represented as tensors, implement classic CNN architectures from scratch, use pre-trained models for transfer learning, and make informed decisions about architecture selection with practical hands-on experience using real vision models.

Let’s begin by understanding what an image really is.