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Impact of Weight Initialization in Neural Nets by lex is a document available to read on EtoBox.

What is Impact of Weight Initialization in Neural Nets about?

1. The document discusses different methods for initializing the weights of a neural network, including zeros initialization, random initialization, and He initialization. 2. Zeros initialization, which sets all weights to zero, is implemented. When used to train a neural network on a classification problem, it results in very poor performance, with an accuracy of only 50% on the training and test sets. 3. With zeros initialization, the cost does not decrease during training and the predictions are all

Author

lex

Language

EN