Epoch in Machine Learning

What is an epoch in ML?

In machine learning, one entire transit of the training data through the algorithm is known as an epoch. The epoch number is a critical hyperparameter for the algorithm. It specifies the number of epochs or full passes of the entire training dataset through the algorithm’s training or learning process. The internal model parameters of the dataset are updated with each epoch.

As a result, the gradient learning algorithm is named after a single batch epoch. An epoch’s batch size is typically one and is always an integer value.

It can alternatively be represented as an epoch-numbered for-loop, with each loop path traversing the complete training dataset.

When using training algorithms, the number of epochs can reach thousands, and the process is programmed to continue until the model error is suitably minimized. Typically, tutorials and examples employ figures such as 10, 100, 1000, or even higher.

For the training process, line plots may be made using machine learning epochs on the X-axis and the skill or model error on the Y-axis. These line graphs are referred to as the algorithm’s learning curve, and they can be used to diagnose issues such as the training set being underfitting, overfitting, or appropriately learned.

Epoch and Batch in machine learning

When a certain amount of samples are processed, the model is updated. This is referred to as the sample batch size. The number of complete passes in the training dataset is equally significant and is referred to as the epoch in machine learning.

  • Batch size is usually 1 and can be equal to or fewer than the sample number in the training dataset. The epoch in a neural network, also known as the epoch training number, is typically an integer value between 1 and infinity.

As a result, the method can be performed for any length of time. A set epoch number and the factor of the rate being zero over time can be used to stop the algorithm from running.

Both batch size and epoch are hyper-parameters in machine learning of learning algorithms, with integer values used by the training model. These values are not discovered by a learning process because they are not internal model parameters and must be specified for the process when training an algorithm on the training dataset.

Key takeaways

  • It is a machine learning term that refers to how many passes the machine learning algorithm has made through the entire training dataset. When there is a big amount of data, it is common to group datasets into batches. Some individuals use the term “iteration” loosely, referring to the process of running one batch through the model as an iteration.
  • In the context of neural networks, it is one cycle in the entire training dataset. Training a network typically takes more epochs. In other words, epoch meaning in a neural network is that if we use more epochs we can expect better generalization when given new input.
  • It is frequently confused with iteration. The number of iterations required to complete one epoch is the number of steps or a number of batches through partitioned packets of training data. One heuristic reason is that it allows the network to see previous data and readjust parameters. This is done so that the model can’t be biased based on the last few data points during training.
  • Be aware that allowing a network to learn the data for multiple epochs does not guarantee that it will converge or improve. While there have been attempts to transform this process into an algorithm, a comprehensive grasp of the data itself is typically required. In other words, choosing the number of epochs required for a network is an art in machine learning.
  • 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 at hand.