Neural Network

Fundamentals

Connected layers of artificial neurons for pattern recognition.

Neural networks consist of weighted connections (neurons) that process inputs and pass signals forward.

Building blocks: Layers (input/hidden/output), activation functions, weights/bias.
Learning: Gradient descent minimizes a loss function.
Variants: Feedforward, recurrent, convolutional, transformer-based.
Challenges: Over/underfitting, hyperparameter tuning, generalization.