Neural Network
A computing system inspired by biological neural networks, consisting of interconnected layers of artificial neurons that learn patterns from data through training.
Architecture
Input layer receives data. Hidden layers perform transformations using weights, biases, and activation functions. Output layer produces predictions. Deep networks have many hidden layers, enabling learning of hierarchical features.
Training
Forward pass: data flows through the network to produce a prediction. Loss calculation: compare prediction with true value. Backpropagation: compute gradients for each weight. Update: adjust weights via gradient descent. Repeat for many iterations.
Types
Feedforward: Information flows in one direction. Convolutional (CNN): Specialized for images. Recurrent (RNN/LSTM): For sequential data. Transformer: Attention-based, dominant in modern AI. Graph Neural Network: For graph-structured data.