Beginner · Fundamentals

Deep Learning

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. A subset of machine learning using neural networks with many layers to learn hierarchical representations from large datasets.

Technical Definition

A subset of machine learning using neural networks with many layers to learn hierarchical representations from large datasets.

How it works

Deep learning refers to neural networks with multiple hidden layers (hence 'deep'). Each layer learns increasingly abstract representations — from pixels to edges to shapes to objects. The depth enables learning complex functions that shallow networks cannot efficiently represent. Breakthroughs in GPU computing, large datasets, and techniques like dropout and batch normalization made training deep networks practical.

Related Concepts

• Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
• Transformer — An architecture that uses self-attention to process sequences in parallel, powering modern language models like GPT and BERT.
• Convolutional Neural Network (CNN) — A neural network that uses learnable filters to detect spatial patterns like edges, textures, and objects in images.