Deep learning is a branch of machine learning that uses artificial neural networks with many layers to learn complex patterns from data. It has become central to modern artificial intelligence because it can automatically extract useful features instead of relying on hand crafted representations.

The core idea is to stack simple computational units, called neurons, into layers. Each layer transforms its input into a more abstract representation. Training adjusts the connections between neurons so that the network’s output matches desired targets. This is typically done with backpropagation and gradient based optimization.

Different architectures have been developed for different data types and tasks. Convolutional neural networks are especially effective for images because they exploit local spatial structure, enabling state of the art performance in classification, detection and segmentation. Recurrent and sequence models process data with temporal or ordered structure, such as text, speech and sensor streams. Encoder decoder frameworks and attention mechanisms extend these models to tasks like translation and summarization. Generative models such as variational autoencoders and generative adversarial networks learn to create realistic new samples that resemble training data.

Research has also addressed practical issues. Regularization methods such as dropout and batch normalization improve generalization and training stability. Advances in hardware accelerators and software frameworks have made it feasible to train very large models on massive datasets. At the same time, issues like data hunger, lack of transparency, vulnerability to adversarial examples and energy consumption remain active topics of investigation. Overall, deep learning has transformed fields from computer vision and natural language processing to medicine, robotics and scientific discovery.