A neural network is a computational model inspired by the human brain, designed to recognize patterns and make decisions. It's a core concept in artificial intelligence and machine learning.

🧠 Key Components

  • Neurons: Basic units that process input data.
    neuron
  • Layers:
    • Input Layer: Receives raw data.
    • Hidden Layers: Processes data through weighted connections.
    • Output Layer: Produces final results.
  • Weights & Biases: Adjustable parameters that determine the strength of connections.

📈 How Neural Networks Work

  1. Data is fed into the input layer.
  2. Each neuron in the hidden layers applies a non-linear transformation to the data.
  3. The output layer aggregates results to make predictions.
  4. Backpropagation adjusts weights to minimize errors.

🌍 Applications of Neural Networks

  • Image Recognition:
    image_recognition
  • Natural Language Processing:
    natural_language_processing
  • Self-Driving Cars:
    self_driving_cars

📘 Further Learning

For a deeper dive into machine learning basics, visit our learn section. Want to explore real-world examples? Check out neural_network_use_cases.

Stay curious! 🚀