What is a Neural Network?

A neural network is a type of machine learning model inspired by the structure and function of biological neural networks in the human brain. It consists of interconnected nodes (artificial neurons) that process information and learn patterns from data.

Key Points:

  • Neural networks excel at tasks like image recognition, natural language processing, and pattern detection.
  • They learn by adjusting the strengths (weights) of the connections between nodes based on example data.
  • Common types include feedforward, convolutional, and recurrent neural networks.

How Neural Networks Work:

  1. Input layer receives raw data (e.g., image pixels, text)
  2. Hidden layers perform computations and transformations on the data
  3. Output layer produces the final result (e.g., classified image, translated text)
  4. During training, weights are adjusted to minimize errors and improve accuracy

Examples:

  • Image classification (identifying objects, faces, etc. in images)
  • Speech recognition (transcribing spoken words to text)
  • Language translation (converting text from one language to another)