What is Epoch in Machine Learning?

In machine learning, whenever you want to train a model with some data, then Epoch refers to one complete pass of the training dataset through the algorithm. Moreover, it takes a few epochs while training a machine learning model, but, in this scenario, you will face an issue while feeding a bunch of training data into the model. This issue happens due to the limitations of computer storage. To overcome this issue, we have to break the training data into small batches according to the computer memory or storage capacity. Then only we can train a machine learning model by feeding these batches without any hassle. This process is called batch in machine learning, and further, when all batches are fed exactly once to train the model, then this entire procedure is known as Epoch in Machine Learning.

Epochs are defined as the total number of iterations for training the machine learning model with all the training data in one cycle. In Epoch, all training data is used exactly once. Further, in other words, Epoch can also be understood as the total number of passes an algorithm has completed around the training dataset. A forward and a backward pass together counted as one pass in training.

Key points about Epoch and Batch in Machine Learning:

There are a few important points that everyone should keep in mind while training a machine learning model. These are as follows:

• Epoch is a machine learning terminology that refers to the number of passes the training data goes through a machine learning algorithm during the entire data points.
• If there is a large amount of data available, then you can divide entire data sets into common groups or batches.
• The process of running one batch through the learning model is known as iteration. In Machine Learning, one cycle in entire training data sets is called an Epoch. However, in ideal conditions, one cycle in entire training data sets is called an Epoch but training a model typically requires multiple numbers of Epochs.
• Better generalization can be achieved with new inputs by using more epochs in the training of the machine learning model.
• Given the complexity and variety of data in real-world applications, hundreds to thousands of epochs may be required to achieve reasonable test data correctness. Furthermore, the term epoch has several definitions depending on the topic.

Why use more than one Epoch?

It may not look correct that passing the entire dataset through an ML algorithm or neural network is not enough, and we need to pass it multiple times to the same algorithm.

So it needs to be kept in mind that to optimize the learning, we use gradient descent, an iterative process. Hence, it is not enough to update the weights with a single pass or one epoch.

Moreover, one epoch may lead to overfitting in the model.

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