Deep Learning

Deep learning refers to neural networks with multiple hidden layers (hence "deep") that can learn increasingly abstract representations of data at each successive layer. This hierarchical feature learning is what distinguishes deep learning from shallow machine learning approaches and enables it to tackle highly complex tasks.

In a deep learning model, early layers might learn simple features such as edges in images or phonemes in speech, while deeper layers compose these into more complex concepts like faces or words. This automatic feature extraction eliminates the need for manual feature engineering, which was a major bottleneck in traditional machine learning pipelines.

Deep learning has driven breakthroughs in computer vision, natural language processing, speech recognition, and generative AI. Architectures such as convolutional neural networks, recurrent neural networks, and transformers are all forms of deep learning that have achieved superhuman performance on specific benchmarks.