Deep learning is a branch of machine learning that uses artificial neural networks with many layers to automatically learn useful representations from data. Instead of relying on hand crafted features, deep learning systems learn hierarchical features directly from raw inputs such as images, sound, text or sensor data.

Convolutional neural networks are widely used for image and video analysis. They exploit spatial structure through convolution and pooling layers, enabling tasks like object recognition, segmentation and detection with high accuracy. Recurrent and sequence models, including variants such as LSTM and GRU, are designed for time dependent data and have been applied to speech recognition, machine translation and forecasting. More recently, attention based architectures and transformers have become central in language processing and multimodal learning.

Training deep networks typically uses gradient based optimization and backpropagation. Large labeled datasets and powerful hardware, especially GPUs and specialized accelerators, are crucial. Techniques such as regularization, dropout, normalization and data augmentation improve generalization and stability. Hyperparameter tuning and careful initialization are important to avoid problems such as vanishing or exploding gradients.

Applications span computer vision, natural language processing, medical diagnosis, autonomous driving, recommendation systems and scientific data analysis. In scientific domains, deep learning can extract structure from complex, noisy measurements, assist in experimental design and accelerate simulations.

Current research addresses interpretability, robustness to adversarial perturbations, data efficiency, uncertainty quantification and integration with physical models. There is growing interest in self supervised and unsupervised methods, which reduce dependence on labeled data. Another active area involves combining deep learning with traditional modeling and domain knowledge to produce more reliable and explainable systems.