What Is Deep Learning?

Deep learning gets its name from the depth of neural networks it employs — models with many hidden layers stacked between input and output. Each layer learns to represent data at a different level of abstraction. In image recognition, for example, early layers might detect edges, middle layers identify shapes and textures, and deeper layers recognize objects and faces. This hierarchical feature learning is what makes deep learning so powerful: rather than requiring humans to hand-engineer features, the network discovers the most useful representations automatically from raw data. Deep learning has driven most of the major AI breakthroughs of the past decade, including large language models, image generation, speech recognition, and game-playing AI. The approach requires large amounts of data and significant computational resources (typically GPUs or TPUs) for training, but once trained, models can perform inference efficiently. Key deep learning architectures include convolutional neural networks (CNNs) for images, recurrent neural networks (RNNs) for sequences, and transformers for language and beyond. The field continues to advance rapidly with new architectures and training techniques.