Neural networks are a cornerstone of modern artificial intelligence, inspired by the structure and function of the human brain. They consist of layers of interconnected nodes (neurons) that process data through weighted connections and activation functions.

Key Concepts

  • Basic Structure:

    • Input layer: Receives raw data.
    • Hidden layers: Process data through non-linear transformations.
    • Output layer: Produces the final result.
    neural_network_structure

  • Learning Mechanism:
    Neural networks adjust weights using backpropagation and gradient descent to minimize errors.

    backpropagation

  • Types of Networks:

    • Feedforward Neural Networks (FNN): Simplest form, data flows in one direction.
    • Recurrent Neural Networks (RNN): Handle sequential data with loops.
    • Convolutional Neural Networks (CNN): Specialized for image processing.
    recurrent_neural_network

Applications

Neural networks are widely used in:

  • Image and video recognition
  • Natural language processing
  • Predictive analytics
  • Autonomous systems
    For deeper insights into deep learning, explore our dedicated resource: /en/resources/knowledge/deep-learning.

Fun Fact 🤖

A single neuron can mimic basic logic gates (AND, OR, NOT), but networks of neurons enable complex pattern recognition!

neural_network_training

For visual learning, check out our interactive demo: /en/demo/neural-networks.