Neural networks are advanced computational models that mimic the human brain's structure, enabling them to capture and model complex, non-linear relationships between inputs and outputs. They consist of layers of perceptrons (neurons) that process inputs through weighted connections.
Perceptron as a Linear Classifier
Neural networks are computational models that mimic the human brain's structure to process information. They consist of units called neurons or perceptrons, which are the fundamental building blocks of neural networks. The training of these networks involves adjusting weights and biases to minimize the error in predictions, a process achieved through algorithms like gradient descent and Newton's method.