Deep learning
Definition
Deep learning uses neural networks with many layers to learn hierarchical representations from data. It has driven progress in vision, language, and other domains by scaling data and compute.
It extends machine learning by using differentiable, layered models (see neural networks) that learn features automatically instead of hand-crafted ones. Depth allows the model to build increasingly abstract representations (e.g. edges → textures → parts → objects in vision).
How it works
Data is fed into the first layer; each layer applies a linear transformation followed by a nonlinearity (e.g. ReLU). Stacking layers produces a representation (embedding) that becomes more abstract in deeper layers. The final layer maps to the output (e.g. class scores or tokens). Training uses backpropagation to compute gradients and gradient descent to update weights. Architectures (CNNs for images, RNNs for sequences, Transformers for both) tailor the connectivity and operations to the data and task.
Use cases
Deep learning is the default for perception and generation when data is abundant and tasks are complex.
- Image recognition, object detection, and segmentation (vision)
- Speech recognition, machine translation, and text generation (language)
- Game playing, robotics control, and simulation (reinforcement learning)
External documentation
- Deep Learning (Goodfellow et al.) — Free online book
- PyTorch – Introduction — Hands-on deep learning