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A neural network is a computing system loosely inspired by the structure of the human brain, composed of interconnected nodes (neurons) organised in layers. Each connection has a weight that adjusts during training, allowing the network to learn complex patterns from data.
Neural networks are the building blocks of deep learning and underpin virtually all cutting-edge AI applications, from image generation to language understanding.
Handwriting recognition on tablets and smartphones uses a neural network to interpret the strokes you draw on screen and convert them into typed text.
Deep Learning
Deep learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns from large amounts of data. The 'deep' refers to the multiple layers that progressively extract higher-level features from raw input.
Machine Learning (ML)
Machine learning is a subset of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following hard-coded rules, ML models identify patterns in data and use them to make predictions or decisions.
Convolutional Neural Network (CNN)
A convolutional neural network is a type of deep learning model specifically designed for processing grid-like data such as images. It uses special layers called convolutional layers that automatically learn to detect visual features like edges, textures, and shapes.
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