Deep Learning
A subset of machine learning using neural networks with multiple layers to learn complex patterns.
Detailed Definition
Deep Learning is a specialized subset of machine learning that uses artificial neural networks with multiple layers (hence 'deep') to model and understand complex patterns in data. Inspired by the structure and function of the human brain, deep learning networks consist of interconnected nodes (neurons) organized in layers that progressively extract higher-level features from raw input data. This approach has revolutionized AI by enabling computers to automatically learn representations from data without extensive manual feature engineering. Deep learning has achieved breakthrough results in computer vision (image recognition), natural language processing (language understanding), speech recognition, and game playing. The technology powers modern AI applications including facial recognition systems, language translators, recommendation engines, and autonomous vehicles. Recent advances in deep learning, particularly transformer architectures, have led to the development of powerful language models like GPT and Claude.
Core TechnologiesMore in this Category
Autoregressive Model
A type of model that predicts the next element in a sequence based on previous elements.
BERT
Bidirectional Encoder Representations from Transformers - a pre-trained language model.
Embedding
A numerical representation of data that captures semantic meaning in a high-dimensional vector space.
GPT (Generative Pre-trained Transformer)
A family of language models that generate human-like text using transformer architecture.
Large Language Model (LLM)
AI models with billions of parameters trained on vast text datasets to understand and generate human language.
Neural Network
A computing system inspired by biological neural networks that learns to perform tasks by analyzing examples.