Neural Network

AI Terms

Neural Network

Definition: A neural network is a computational model inspired by the structure and function of the human brain.

Detailed Description:

Neural networks are composed of interconnected nodes called neurons, which process and transmit information. They are organized into layers, including input, hidden, and output layers. Neural networks learn by adjusting the weights and biases of the connections between neurons through a process called backpropagation.

Neural networks have been successfully applied to a wide range of tasks, including:

  • Image recognition: Identifying objects, faces, and scenes within images.
  • Natural language processing: Understanding and generating human language.
  • Speech recognition: Transcribing spoken language into text.
  • Game playing: Mastering complex games like Go and chess.

Related Terms:

FAQs:

  1. What is the difference between a neural network and a traditional computer program?

    • Neural networks are inspired by the human brain and can learn from data, while traditional computer programs follow a set of predefined rules.
  2. How do neural networks learn?

    • Neural networks learn by adjusting the weights and biases of their connections through a process called backpropagation.
  3. What are some challenges in training neural networks?

    • Challenges include obtaining sufficient training data, avoiding overfitting, and choosing appropriate hyperparameters.

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