What is the difference between iterations and epochs in Convolution neural networks?

https://stats.stackexchange.com/questions/164876/tradeoff-batch-size-vs-number-of-iterations-to-train-a-neural-network

It has been observed in practice that when using a larger batch there is a significant degradation in the quality of the model, as measured by its ability to generalize.

https://stackoverflow.com/questions/4752626/epoch-vs-iteration-when-training-neural-networks/31842945

In the neural network terminology:

• one epoch = one forward pass and one backward pass of all the training examples
• batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you‘ll need.
• number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes).

Example: if you have 1000 training examples, and your batch size is 500, then it will take 2 iterations to complete 1 epoch.