Feedforward Neural Networks and Backpropagation - Part 1

Summary

This video lecture delves into the workings of multi-layer perceptrons (MLPs), a type of feedforward neural network. It explains how these networks, composed of layers of neurons, can approximate complex functions by mapping input to output. The lecture covers the process of training MLPs using gradient descent, focusing on how the loss function is minimized through iterative updates of weights and biases. By employing backpropagation and the chain rule, the network learns to adjust its parameters to improve performance. Key concepts like loss functions, gradient descent, and the importance of the learning rate are also discussed.

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