Computer Vision

About this track

A convolutional network is usually drawn as a row of coloured boxes that explains nothing. This track takes the boxes apart: what a filter is, what it produces, why the output shrinks, what padding restores, and what a stride costs you.

It starts with how an image is stored at all - grayscale intensities, then RGB channels - and finishes on the practical layer: augmentation, data loaders and transfer learning.

The stack is taken apart rather than drawn as coloured boxes: what a filter contains, what a feature map records, what pooling discards, and how many parameters each of those choices actually costs.

What you will be able to do

• Explain what a convolutional filter is and why it spans every input channel.
• Compute a layer's output size from the input, kernel, padding and stride.
• Count a convolutional layer's parameters, and say why the image size never appears.
• Describe what pooling buys and what it destroys.
• Say why weight sharing gives both a parameter saving and translation equivariance.

How the track is ordered

It starts below the network, with how an image is stored at all - grayscale intensities, then RGB channels - because a filter's depth only makes sense once channels do. Then the convolution itself: edge detection by hand, feature maps, padding, stride, pooling and parameter counting. After that the components that make a real network work, including ReLU, dense layers and residual connections, and finally the applied layer: augmentation, data loaders, transfer learning, object detection and segmentation. The deep learning track is assumed.

Where this leads

Transfer learning is where most practical vision work actually starts, and the generative AI track covers the modern architectures that now compete with convolutional networks on the same tasks.