Deep learning revolutionized AI by enabling machines to automatically learn features from raw data. Unlike traditional machine learning, deep learning models can discover intricate patterns in large datasets without manual feature engineering.
Why “Deep”?
The term refers to the multiple layers (depth) in neural networks. Each layer learns progressively more abstract representations, from simple edges in images to complex concepts.
Breakthroughs
Deep learning enabled major advances in computer vision (ImageNet), natural language processing (GPT, BERT), speech recognition, game playing (AlphaGo), and generative AI.
Tags
Related Terms
Backpropagation
The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.
Neural Network
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
Transformer
A neural network architecture introduced in 'Attention is All You Need' (2017) that relies entirely on self-attention mechanisms, becoming the foundation for modern LLMs.