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Neural network fundamentals, backpropagation, CNNs, and modern architectures that power computer vision.
Deep learning has revolutionized computer vision, achieving superhuman performance on tasks that once seemed impossible. But neural networks aren't magic—they're built on simple mathematical principles.
What is this chapter about? We demystify deep learning by building up from the basics: neurons, layers, and how networks learn through backpropagation. Then we explore Convolutional Neural Networks (CNNs)—the architecture specifically designed for images.
Why does this matter? Nearly every state-of-the-art computer vision system uses deep learning:
How the topics connect: We start with neural network fundamentals—understanding what neurons compute and how layers compose. Then backpropagation explains how networks learn from data. CNNs introduce convolution layers that exploit image structure. Finally, we survey modern architectures that achieve state-of-the-art results.
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Neurons, activation functions, and loss functions — the atomic units that compose every deep learning model.
The chain rule applied to computational graphs — how networks learn by propagating error signals backward through layers.
Convolutions, pooling, and receptive fields — the architecture designed to exploit the spatial structure of images.
ResNets, transformers, batch normalization, and dropout — the innovations that enable training very deep networks at scale.
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