How does batch normalization affect the learning rate and the weight decay in neural networks?
Batch normalization is a technique that helps neural networks train faster and more stably by reducing the internal covariate shift. This means that the distribution of the inputs to each layer of the network does not change significantly during training, which can cause problems for gradient-based optimization. In this article, you will learn how batch normalization affects the learning rate and the weight decay in neural networks, and why these are important hyperparameters to tune.
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Giovanni Sisinna??Portfolio-Program-Project Management, Technological Innovation, Management Consulting, Generative AI, Artificial…
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Daniel Zalda?a??Artificial Intelligence | Algorithms | Thought Leadership1 个答复
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Nebojsha Antic ???? Business Intelligence Developer | ?? Certified Google Professional Cloud Architect and Data Engineer | Microsoft ??…