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Part III – CNN Architectures and Concepts

“The data flows in—but what happens next? This is where filters activate, features emerge, and vision becomes understanding.”

Why This Part Matters

You’ve resized, normalized, and batched your image data.

But how does a neural network actually see a dog’s ear, detect a road sign, or recognize a handwritten digit?

It happens through the architecture: the convolutional layers, activations, pooling operations, and more that together form a trainable perception system. This part walks you through that internal machinery.

By the end of Part III, you’ll be able to:

  • Build CNN architectures from scratch
  • Understand how each layer transforms the input
  • Spot design patterns used in popular models
  • Interpret what each layer contributes to learning

What You’ll Master in This Part

  • The anatomy of CNN layers—what they do and how to use them
  • Common terms like kernel, stride, padding, feature map, and filter
  • The logic behind forward passes and forward()/call() methods
  • How to inspect a model’s structure and parameter count
  • CNN design patterns used in real-world architectures like LeNet and Mini-VGG

Each chapter dives deeper into the building blocks, visualizes internal flows, and compares implementation across PyTorch and TensorFlow—so you gain full control over how your models are structured and trained.

Chapter Breakdown

Chapter Title What You’ll Learn
8 Understanding CNN Layers Kernels, strides, padding, pooling, activations, and normalization layers
9 The CNN Vocabulary (Terms Demystified) What filters, channels, and feature maps really mean—visually and in code
10 Writing the forward() / call() Function Model subclassing, layer flow, common mistakes, debug-friendly architectures
11 Model Summary and Parameter Inspection Counting parameters, freezing/unfreezing layers, weight inspection
12 Building Your First CNN: Patterns and Pitfalls Hands-on with LeNet, Mini-VGG, filter size strategies, design mistakes to avoid

Key Ideas That Tie This Part Together

  1. Layers aren’t black boxes—they’re geometric transformers.

  2. Each convolution shrinks or expands your input in predictable ways. Knowing how lets you design with intention.

  3. Your model is a blueprint.

  4. Whether you’re subclassing in PyTorch or TensorFlow, you need to know how tensors flow from layer to layer—and why.

  5. Architecture ≠ Accuracy—but it enables it.

  6. Fancy models don’t guarantee results, but clear, modular, well-planned architectures make success reproducible.

What Makes This Part Unique

Most books or tutorials show a pre-written CNN, but they rarely:

  • Walk you through layer-by-layer decisions
  • Explain what happens visually inside each operation
  • Compare Conv2d vs Conv2D with actual shape flows
  • Show what happens when you pass an image into your forward() method

This part does.

Learning Goal for This Part

By the end of Part III, you should be able to:

  • Construct custom CNNs for new tasks
  • Modify and fine-tune pretrained architectures
  • Understand internal layer behavior and diagnose structural bugs
  • Experiment confidently with kernel sizes, channel depths, and activation strategies