ScienceVerseLearning path: Gradient descent
The concepts to learn before Gradient descent, in order. Each step links to its page and, where one exists, its quiz.
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Your path
- Machine learningDirect prerequisite
Machine learning is the part of AI in which a program improves at a task through experience — data — instead of following rules written in advance. Supervised learning learns from labelled examples, unsupervised learning finds structure in unlabelled data, and reinforcement learning learns which actions earn reward by trial and error. The real test of a learned model is not how well it fits its training data but how well it performs on new data it has never seen.
- Gradient descentYour goal
Gradient descent is the step-by-step method most machine-learning systems use to learn: it measures how the error changes as each parameter changes (the gradient) and nudges every parameter a little way downhill. The learning rate sets the size of each step — too small and learning crawls, too large and it overshoots and can blow up. Variants such as stochastic gradient descent, momentum and Adam make it practical for models with millions of parameters.