Multilayer networks, Backpropagation

Neural Networks

Explore the building blocks of artificial intelligence

Interactive Neural Network

Visualize how neural networks process information through layers of interconnected neurons.

Network Configuration
Network Weights

Weights determine the strength of connections between neurons. During training, these weights are adjusted to minimize error.

Click on connections to see weight values

Activation Functions

Compare different activation functions and their effects on neural network behavior.

function Selection
Activation function Properties

Sigmoid: Smooth, bounded between 0 and 1. Good for binary classification but suffers from vanishing gradients.

Network Architecture

Input Layer: 3 neurons

Hidden Layer: 4 neurons

Output Layer: 2 neurons

Total Parameters: 26

Key Concepts

  • Neuron: Basic processing unit that receives inputs and produces output
  • Weights: Parameters that determine connection strength
  • Bias: Additional parameter that shifts the activation function
  • Activation: function that introduces non-linearity

Training Process

ncw: 0

Loss: -

Accuracy: -

Backpropagation Steps:
  1. Forward pass: Calculate predictions
  2. Calculate loss: Compare with true values
  3. Backward pass: Compute gradients
  4. Update weights: Adjust parameters

Applications

  • Image Recognition
  • Natural Language Processing
  • Speech Recognition
  • Medical Diagnosis
  • Financial Forecasting
  • Game Playing (AlphaGo)