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In courses there is nothing about epochs, but in practice they are everywhere used. Why do we need them if the optimizer finds the best weight in one pass. Why does the model improve?
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Why do we need several epochs? Because gradient descent are iterative algorithms. It improves, but it just gets there in tiny steps. It only uses tiny steps, because it can only use local information.
An epoch means training the neural network with all the training data for one cycle. In an epoch, we use all of the data exactly once. A forward pass and a backward pass together are counted as one pass: An epoch is made up of one or more batches, where we use a part of the dataset to train the neural network.
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The right number of epochs depends on the inherent perplexity (or complexity) of your dataset. A good rule of thumb is to start with a value that is 3 times the number of columns in your data. If you find that the model is still improving after all epochs complete, try again with a higher value.
Generally whenever you want to optimize you use gradient descent. Gradient descent has a parameter called learning rate. In one iteration alone you can not guarantee that the gradient descent algorithm would converge to a local minima with the specified learning rate. That is the reason why you iterate again for the gradient descent to converge better.
Its also a good practice to change learning rates per epoch by observing the learning curves for better convergence.
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