Forward and Backpropagation
Manage episode 444738225 series 3605861
Let's get into the core processes of forward propagation and backpropagation in neural networks, which form the foundation of training these models. Forward propagation involves calculating the outputs of a neural network, starting with the input layer and moving towards the output layer. Backpropagation then calculates the gradients of the network's parameters, essential for updating these parameters during optimization. The text illustrates these concepts through the use of computational graphs, which visually represent the flow of information and calculations within the network. The text emphasizes the importance of automatic differentiation, which allows for efficient calculation of gradients without manual derivation. Finally, the text highlights the interdependence of forward and backward propagation during training, leading to significantly higher memory requirements compared to prediction.
Read more: https://d2l.ai/chapter_multilayer-perceptrons/backprop.html
37 επεισόδια