Neural Network
A computing system inspired by biological neural networks that learns to perform tasks by analyzing examples.
Detailed Definition
A Neural Network is a computing system inspired by the biological neural networks that constitute the human brain. These artificial networks consist of interconnected nodes (artificial neurons) organized in layers that process information by passing signals between them. Each connection has an associated weight that adjusts as the network learns, enabling it to recognize patterns and make predictions. Neural networks learn through a process called training, where they analyze large amounts of example data and adjust their internal parameters to minimize errors. Modern neural networks can have millions or billions of parameters and multiple hidden layers (deep neural networks), enabling them to learn complex patterns and representations. They form the foundation of most contemporary AI systems, powering applications in image recognition, natural language processing, speech recognition, and many other domains. Different architectures like convolutional neural networks (CNNs) for images and transformers for language have been developed to optimize performance for specific types of data and tasks.
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.
Deep Learning
A subset of machine learning using neural networks with multiple layers to learn complex patterns.
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.