Convolutional Neural Networks
Phase: 3 | Status: ✅ Complete | Prerequisites: 13 Neural Networks
Overview
Convolution (cross-correlation), pooling, feature maps, translation equivariance, parameter sharing, and building a small CNN from scratch. Covers the forward and backward pass of convolutional and pooling layers, receptive fields, and common architecture patterns (LeNet onward).
Contents
| # | File | Type | Description |
|---|---|---|---|
| 1 | theory.md |
Theory | Convolution operation, padding, stride, pooling, backprop through conv, parameter counting, receptive field, architectures, failure cases |
| 2 | first_principles.ipynb |
Computation | Conv2D and MaxPool2D from scratch, edge detection demo, small CNN on pattern task, PyTorch comparison, filter visualization, shuffled-pixel failure |
| 3 | exercises.ipynb |
Practice | Hand convolution calculation, cross-correlation coding task, conceptual questions on parameter sharing and receptive field |
Connections
- Prereqs: 13 Neural Networks
- Synthesis: Loss Functions, Optimization Methods
- Next: 15 RNN/LSTM, 16 Transformer