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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.