Neural Network
A computing system inspired by biological neural networks, consisting of interconnected layers of nodes that learn patterns from data through training.
In Depth
A neural network is a computational model inspired by the structure and function of biological neural systems, consisting of interconnected layers of artificial neurons (nodes) that process information by learning patterns from data. Neural networks form the foundational architecture underlying virtually all modern AI systems, from image recognition and natural language processing to autonomous vehicles and drug discovery.
The basic structure of a neural network consists of an input layer that receives data, one or more hidden layers that transform the data through weighted connections and nonlinear activation functions, and an output layer that produces predictions. During training, the network adjusts its connection weights through backpropagation, an algorithm that computes how much each weight contributed to the prediction error and updates weights in the direction that reduces that error.
Neural network architectures have diversified to address different types of problems. Feedforward networks process data in one direction for classification and regression. Convolutional neural networks (CNNs) use spatial filters for image and signal processing. Recurrent neural networks (RNNs) and their variants (LSTM, GRU) process sequential data with memory of previous inputs. Transformer networks use attention mechanisms for parallel sequence processing. Graph neural networks operate on graph-structured data for social network analysis and molecular modeling.
The practical success of neural networks depends on architecture selection, sufficient quality training data, appropriate loss functions and optimization algorithms, regularization techniques to prevent overfitting, and sufficient compute resources. Deep neural networks with many layers can learn hierarchical representations of increasing abstraction, enabling them to model complex patterns that simpler models cannot capture. Understanding neural network fundamentals is essential for AI practitioners making decisions about model architecture, training strategy, and deployment.
Related Terms
Deep Learning
A subset of machine learning using neural networks with many layers to automatically learn hierarchical representations from large amounts of data.
Transformer
A neural network architecture based on self-attention mechanisms that processes input sequences in parallel, forming the foundation of modern large language models.
Machine Learning
A branch of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed for each scenario.
Attention Mechanism
A neural network component that dynamically weighs the importance of different input elements when producing an output, enabling models to focus on relevant context.
Training Data
The curated dataset used to train or fine-tune machine learning models, directly determining model capabilities, biases, and limitations.
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