Artificial Neural Networks

Machine learning algorithms inspired by the human brain, used to solve complex problems.

Artificial Neural Networks

Artificial Neural Networks (ANN)

Artificial neural networks are computational models that mimic the way the human brain processes information. These networks operate through interconnected nodes, or artificial neurons. Each connection has a weight that represents the strength of influence on other neurons in the network.

Basic Components:

  • Neurons: Receive, process, and produce output data.
  • Connections: Represent relationships between neurons and are associated with weights.
  • Weights: Determine the strength of connections and are adjusted during the learning process.
  • Activation Functions: Determine the output of a neuron.

Working Principle:

  1. Input data is fed into the network.
  2. Data propagates through the neurons in the network.
  3. Each neuron processes its inputs according to their weights and applies an activation function.
  4. The network produces an output.
  5. The network calculates errors by comparing its outputs with actual values.
  6. Weights are adjusted to reduce errors (backpropagation).

Applications:

  • Image recognition
  • Natural language processing
  • Prediction
  • Control systems
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Peri Peri

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