Term tag: CNN

Computer Vision (CV)

How computers turn a grid of pixel values into labels, boxes and masks — the task ladder, the 2012 hinge, and where vision is reliable versus brittle.

Convolution

Convolution slides a small kernel over an image, multiplying and summing each patch into one output number. The arithmetic of stride, padding and kernels.

Convolutional Neural Network (CNN)

The architecture that assumes image features are local and position-independent — how the stack builds a hierarchy, and where CNNs still beat transformers.

Layers in Neural Networks

A layer is one weight matrix plus a nonlinearity. How it differs from a neuron and a parameter, why stacking helps, and how many layers real models have.

Pooling

A downsampling operation in CNNs that shrinks a feature map by summarizing each small region into one value, cutting resolution and compute.

Residual Connections (Skip Connections)

Residual connections add a block's input back to its output — F(x) + x — the trick that lets gradients flow through deep nets and made transformers trainable.

Vision Transformer (ViT)

A Vision Transformer (ViT) cuts an image into fixed patches, embeds each as a token, and runs a transformer encoder — trading CNN inductive bias for scale.