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Deconstructing CNNs from Scratch

Explore the mathematics of convolutions, weight sharing, and hierarchical feature learning — built entirely from scratch in pure PyTorch.

Part 1

The Math of Convolutions

Breaking down the 2D convolution operation, local connectivity, weight sharing, pooling, and the inductive biases that make CNNs powerful for visual data.

Part 2

PyTorch Implementation

Building Conv2D, pooling layers, and complete CNN architectures (LeNet-5, SimpleCNN, DeepCNN) entirely from scratch in PyTorch.
View Code on GitHub

Part 3

Training & Visualizing Features

Training on MNIST, visualizing learned convolutional filters, and analyzing hierarchical feature map activations to peer inside the black box.