Machine learning – EPOCH VS ITERATION Time to train neural networks

When training a multilayer perceptron, what is the difference between epoch and iteration?
In neural network terms:

>One period = one forward path and A backward pass through all training examples
>Batch size=The number of training samples in a forward/backward pass. The larger the batch size, the more memory space you need.
>Number of iterations=Pass The number of times, each pass is an example of using the number of [batch size]. To be clear, one pass = one forward pass, one backward pass (we don’t count forward and backward passes as two different passes).

Example: If you have 1000 training examples and the batch size is 500, you need 2 iterations to complete 1 epoch.

For reference only: Tradeoff batch size vs. number of iterations to train a neural network

The term “batch processing” is ambiguous: some people use it to specify the entire training set, some people use it to refer to a training example in forward/backward Number (as I did in this answer). To avoid this ambiguity and make it clear that the batch corresponds to the number of training samples in a forward/backward pass, the term mini-batch can be used.

When training a multilayer perceptron, what is the difference between epoch and iteration?

In neural network terms:

>One period = one forward path and one backward pass through all training examples
> Batch size = the number of training samples in a forward/backward pass. The larger the batch size, the larger the memory space you need.
>Number of iterations = the number of passes, each pass uses the number of [batch size] Example. To be clear, one pass = one forward pass and one backward pass (we don’t count forward and backward passes as two different passes).

Example: If you have 1000 training examples, and the batch size is 500, it takes 2 iterations to complete 1 epoch.

For reference only: Tradeoff batch size vs. number of iterations to train a neural network

The term “batch processing” is ambiguous: some people use it to specify the entire training set, some people use it to refer to the number of training examples in a forward/backward (as I did in this answer) In order to avoid this ambiguity and make it clear that the batch corresponds to the number of training samples in a forward/backward pass, the term mini-batch can be used.

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