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What is Neural Network Training Explained about?

The document discusses how neural networks are trained. It begins with an agenda that outlines topics including a simpler model, activation functions, and overfitting. It then explains how a neural network learns by adjusting its weights and biases to minimize a loss function through gradient descent. Different activation functions like ReLU and sigmoid are also introduced. Optimizers help neural networks learn more efficiently by determining how far to move along the gradient direction each step.

Author

Praveen Singh

Language

EN