Deep Learning

Overview

Deep learning is the branch of machine learning that uses neural networks with many layers — hence "deep" — to progressively extract higher-level features from raw input. A deep learning model might learn to recognize edges in the first layer, shapes in the second, objects in the third, and complex scenes in deeper layers.

What Makes It "Deep"

The "depth" in deep learning refers to the number of layers in the neural network. While a simple neural network might have one or two hidden layers, deep learning models can have dozens, hundreds, or even thousands of layers. Each layer transforms its input into a slightly more abstract representation.

Key Architectures

Convolutional Neural Networks (CNNs)

Specialized for processing grid-like data such as images. CNNs use convolutional layers to automatically learn spatial hierarchies of features.

Recurrent Neural Networks (RNNs)

Designed for sequential data like text and time series. RNNs maintain a hidden state that captures information from previous steps, though they struggle with very long sequences.

Transformers

The dominant architecture for language and increasingly for vision tasks. Transformers use self-attention mechanisms to process entire sequences in parallel, solving the long-range dependency problems of RNNs.

Generative Adversarial Networks (GANs)

Two neural networks trained in competition — a generator that creates data and a discriminator that evaluates it — producing remarkably realistic synthetic data.

Deep Learning and Context

Deep learning models are inherently context-dependent. The deeper layers of a neural network learn increasingly abstract contextual representations. In transformers specifically, the self-attention mechanism creates rich contextual embeddings where each token's representation is influenced by all other tokens in the sequence.