Introduction

Deep learning uses neural networks with many layers to learn representations directly from raw data. By stacking simple transformations, these models build up increasingly abstract features—edges to shapes to objects in images, characters to words to meaning in text—without the manual feature engineering that classical methods often require. This ability to learn representations is what makes deep learning so general.

Its rise was driven less by a single breakthrough than by the convergence of large datasets, parallel hardware, and reliable training techniques. This section explains how these networks are built, trained, and adapted across domains.

How Networks Learn

A neural network is a composition of layers, each applying a linear transformation followed by a nonlinear activation. Training adjusts the network's weights to reduce a loss function using gradient descent, with gradients computed efficiently by backpropagation. Choices such as learning rate, batch size, initialization, and optimizer materially affect whether training succeeds, and much of practical deep learning is the craft of making optimization stable and efficient.

Architectures

Different data and tasks favor different structures. Convolutional neural networks exploit spatial locality and excel at images. Recurrent networks and their gated variants process sequences, though they have largely been superseded for many tasks by transformers, whose attention mechanism captures long-range dependencies and scales effectively. Embeddings, residual connections, and normalization layers are recurring building blocks that appear across these families.

Training in Practice

Deep models are powerful enough to memorize their training data, so generalization depends on regularization: dropout, weight decay, data augmentation, and early stopping all help. Transfer learning—starting from a model pretrained on large data and adapting it to a specific task—has become the default, dramatically reducing the data and compute needed for new problems. Reliable training also depends on hardware: GPUs and specialized accelerators, along with mixed-precision and distributed techniques, make large models feasible.

Toward Larger Models

Scaling networks, data, and compute has repeatedly unlocked new capabilities. The same architectures introduced here—transformers in particular—underpin the large generative models covered later in the library. Understanding the mechanics of training and regularization at a smaller scale is the foundation for reasoning about systems with billions of parameters.