MACHINE LEARNING

Detailed explanation-1: -This also means that one epoch will involve 40 batches or 40 updates to the model. With 1, 000 epochs, the model will be exposed to or pass through the whole dataset 1, 000 times. That is a total of 40, 000 batches during the entire training process.

Detailed explanation-2: -We can divide the dataset of 2000 examples into batches of 500 then it will take 4 iterations to complete 1 epoch.

Detailed explanation-3: -Say you have a dataset of 10 examples (or samples). You have a batch size of 2, and you’ve specified you want the algorithm to run for 3 epochs. Therefore, in each epoch, you have 5 batches (10/2 = 5). Each batch gets passed through the algorithm, therefore you have 5 iterations per epoch.

Detailed explanation-4: -The batch size affects some indicators such as overall training time, training time per epoch, quality of the model, and similar. Usually, we chose the batch size as a power of two, in the range between 16 and 512. But generally, the size of 32 is a rule of thumb and a good initial choice.

Detailed explanation-5: -One epoch means that each sample in the training dataset has had an opportunity to update the internal model parameters. An epoch is comprised of one or more batches. For example, as above, an epoch that has one batch is called the batch gradient descent learning algorithm.