What Is an Epoch?
In artificial neural networks, an epoch is one loop of the whole training dataset. Training a neural network typically takes many epochs.
Simply put, if we supply a neural network with training data in diverse patterns over more than one epoch, we expect improved generalization when we give it a fresh unobserved input (test data).
Batch Gradient Descent
The dataset’s underlying parameters of the model are changed with each epoch.
As a result, the batch gradient descent learning algorithm is named after each batch of the epoch. Batch size is usually 1 or greater and is always an integer value in the epoch number.
It may alternatively be represented as a for-loop with a certain number, with each loop route traversing the whole training dataset.
Balancing Iterations and Data Exploration
When the sample “batch size” value is given as one, the for-loop contains a layer that enables it to run through a specified sample in a single batch.
Establishing how many epochs a model should execute to train is reliant on several parameters linked to both the data and the model’s objective.
To convert this procedure into an algorithm, a thorough understanding of the data is typically required.
Batch Size in Neural Network Training
When a complete dataset is transmitted forward and then back through the neural network, it is called an Epoch.
We break the epoch into multiple smaller batches because one epoch is too large to send to the computer all at once.