ScienceVerseLearning path: Computer vision
The concepts to learn before Computer vision, in order. Each step links to its page and, where one exists, its quiz.
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Your path
- Neural networksDirect prerequisite
An artificial neural network is a mathematical function built from many simple units arranged in layers: each unit multiplies its inputs by weights, adds a bias and passes the total through an activation function. Stacking layers lets the network represent patterns — such as XOR — that no single linear unit can. The weights are not written by hand but learned from data, usually by back-propagation and gradient descent; the networks are inspired by the brain but are not realistic models of it.
- Training dataDirect prerequisite
Training data is the set of examples a machine-learning model learns from, and it shapes everything the model can and cannot do. Data is usually split into training, validation and test sets so that the model is judged on examples it never learned from. Gaps, mislabelled examples and skewed samples in the data become gaps, errors and biases in the model, which is why datasets need documenting and checking like any other scientific instrument.
- Computer visionYour goal
Computer vision is the field of getting machines to extract useful information from images and video — recognising objects, reading text, finding faces. To a computer an image is a grid of numbers; convolutional neural networks learn small filters that detect edges and textures and combine them into larger patterns. The organisers of the ImageNet challenge describe 2012 as a turning point, after which the vast majority of entries used deep convolutional networks. High benchmark scores still hide real failure modes: tiny engineered perturbations can fool models, and systems trained or tested on skewed data can be far less accurate for some groups of people.