Neural Network
A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons) that process information through weighted connections and activation functions.
Detailed Explanation
Neural Networks are computational models inspired by the human brain's structure. They consist of layers of interconnected nodes (artificial neurons) that process information. Each connection has a weight that adjusts during training, allowing the network to learn patterns from data. Information flows from input layers through hidden layers to output layers, with each neuron applying an activation function to determine its output. Neural networks excel at recognizing complex patterns in data, making them foundational to modern AI applications like image recognition, speech processing, and natural language understanding.
Real-World Examples
Image Classification
Social MediaPinterest uses neural networks to automatically tag and categorize 200+ billion images, enabling visual search that helps users discover products with 85% accuracy.
Credit Scoring
FinanceFinancial institutions use neural networks to assess credit risk by analyzing hundreds of variables, improving loan approval accuracy by 25% while reducing defaults.
Frequently Asked Questions
Q:How do neural networks learn?
Through a process called backpropagation. The network makes predictions, compares them to actual results, calculates error, and adjusts weights backward through layers to minimize error. This process repeats thousands of times until the network achieves desired accuracy.
Related Terms
Machine Learning (ML)
A subset of AI that enables computers to learn from data and improve their performance over time without being explicitly programmed for every scenario.
Deep Learning
A subset of machine learning that uses artificial neural networks with multiple layers to automatically learn hierarchical representations of data, enabling breakthrough performance in image, speech, and language tasks.
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