Discover the role of batch normalization in streamlining neural network training and improving model performance
Batch normalization has become a very important technique for training neural networks in recent years. It makes training much more efficient and stable, which is a crucial factor, especially for large and deep networks. It was originally introduced to solve the problem of internal covariance shift.
This article will examine the problems involved in training neural networks and how batch normalization can solve them. We will describe the process in detail and show how batch normalization can be implemented in Python and integrated into existing models. We will also consider the advantages and disadvantages of this method to determine whether it makes sense to use it.
When training a deep neural network, backpropagation occurs after each run. The prediction error runs through the network layer by layer from behind. During this process, the weights of the individual neurons are then changed so that the error is reduced as quickly as possible. This changes the weights assuming that all other layers remain the…