Gradient Descent is an optimization and learning technique used to train machine learning systems. As an optimal algorithm, it is utilized in artificial intelligence, deep learning, and neural networks. The algorithm’s purpose is to find the model’s internal parameters, such as logarithmic loss or mean squared error, and compare them to other performance metrics. The batch size and epoch in ML are two hyperparameters of the stochastic gradient descent learning process. Integer values are used for these hyperparameters that behave identically. Let’s look at the distinctions between them.

Epochs

One complete run of the learning dataset through the whole algorithm is referred to as an epoch in ML. The method’s number of iterations is a key hyperparameter. For the entire training dataset, it identifies the amount of epochs or full passes through the algorithm’s learning phase. The dataset’s parameters of the underlying model are modified with each epoch. As an outcome, a batch-type epoch is labeled after the batch descent gradient learning technique. An epoch’s batch size is normally one or more, and the epoch number is always a positive integer.

It may alternatively be seen as a loop with a certain epoch number, route exploring the full training data. When the sample’s “batch size” number is provided as one, the loop contains a nested loop that allows it to run through a set number of samples in a single pack. When using learning methods, the number of epochs can reach a high number. An algorithm is designed to continue.

The distinction between epoch and batch

Before the model is changed, the batch size is the number of samples handled.

The number of epochs is the total number of times the training dataset has been traversed.

Batch size must be greater than one and less than or equal to the number of samples in the training dataset.

The number of epochs can be adjusted to anything from one to infinity. You may run the method indefinitely and even terminate it using criteria other than a predetermined number of epochs, such as a change (or lack thereof) in model error over time.

They’re both numeric values, and they’re both learning algorithm hyperparameters, i.e. learning process parameters, not internal model parameters discovered by the learning process.

For a learning method, you must provide the batch size and the number of epochs.

There are no hard and fast rules for configuring these settings. You must experiment with several settings to see which one works best for your situation.

Conclusion

For comparing gradient descent in an epoch to batches in ML, one may argue that the descent stochastic method employs data for training and an iterative learning process when it’s time to update the model. Before the internal parameters of the model are changed to work through the batch, the batch size is a descent parameter that trains the training samples numbers. The epoch number is another gradient parameter that specifies the number of full passes while traversing over training datasets.