Convolutions from Scratch
A convolution is a tiny dense layer you slide across an image, sharing the same weights at every location.
Prerequisites: Phase 3 (Deep Learning Core), Phase 4 Lesson 01 (Image Fundamentals)
#Learning Objectives
- Implement 2D convolution from scratch using only NumPy, including the nested-loop version and a vectorised
im2col version
- Compute output spatial size for any combination of input size, kernel size, padding, and stride, and justify the
(H - K + 2P) / S + 1 formula
- Hand-design kernels (edge, blur, sharpen, Sobel) and explain why each one produces the pattern of activations it does
- Stack convolutions into a feature extractor and connect the depth-of-the-stack to the size of the receptive field
Keep going. The rest of this lesson is one step away.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.