fran0x · March 18, 2018 20:42
diff --git a/fundamentals_deep_learning b/fundamentals_deep_learning
 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.
	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.