Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to model complex patterns in large datasets. Inspired by the structure of the human brain, deep learning models automatically learn hierarchical representations of data, progressing from simple features in early layers to increasingly abstract concepts in deeper layers, without requiring manual feature engineering.
The most common deep learning architectures include convolutional neural networks (CNNs) for image and video processing, recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data, and transformer models that power modern large language models (LLMs) like GPT and Claude. Frameworks such as TensorFlow, PyTorch, and JAX provide the computational tools needed to build, train, and deploy these models.
Deep learning is behind many of the biggest AI advances in recent years. It powers natural language processing systems that understand and generate human language, computer vision applications that rival human perception, speech recognition, autonomous driving technologies, and generative AI models that create text, images, music, and code.
Training deep learning models requires substantial computational resources, large datasets, and specialized expertise. That said, techniques like transfer learning and fine-tuning pre-trained models have made it much more accessible. Organizations can now build sophisticated AI features by adapting existing models rather than training from scratch, which cuts down on both cost and time considerably.