Deep Learning

Overview

Deep learning uses compositions of parameterized transformations to learn multiple levels of representation. Its progress comes from the interaction of architectures, large datasets, hardware, optimization methods, and increasingly careful evaluation.

What it is

A deep neural network is a model with many layers whose parameters are learned from data. Layers can transform raw inputs into features useful for a task, reducing the need to specify every relevant pattern by hand.

How it works

Inputs pass forward through a network to produce an output. A loss compares that output with a target, and gradient-based optimization adjusts the parameters. At scale, pretraining, transfer learning, augmentation, and distributed computation make it possible to reuse representations across tasks.

Key concepts

• Backpropagation and gradient descent
• Convolution and attention
• Pretraining and transfer learning
• Embeddings
• Scaling and compute
• Overfitting and regularization

Current research questions

• Why do large models generalize despite interpolation of their training data?
• How can training become more efficient in data, energy, and hardware?
• What representations are learned, and how stable are they under shifts?
• How can deep models incorporate structure, constraints, or reliable memory?

Applications

• Image and speech recognition
• Protein and materials modeling
• Document understanding
• Autonomous perception
• Generative media tools

Relevant research papers

A live selection from the ScholarLens research index. Open any result to read its full paper page and use the Reading Assistant where available.