What Is an Epoch in Machine Learning?

An epoch in machine learning refers to one pass through the training data. Your epoch count affects your model's computational efficiency, performance, and accuracy. By understanding what an epoch is and how it fits into machine learning, you can make educated decisions about the type of epoch training you choose and how to use this skill to optimize your machine learning model performance.  

What is an epoch in machine learning?

In machine learning contexts, one epoch is a complete pass through your training data set. When training your model, you'll need to set hyperparameters, which are variables responsible for different aspects of your training. These variables essentially guide your model through the learning process and help you achieve the most effective end result. You might set three common hyperparameters: learning rate, batch size, and epochs. 

• Learning rate: How quickly the model adjusts parameter weights and "learns" from previous iterations 

• Batch size: How many examples the model processes during each iteration of training before updating its parameters

• Epoch count: How many times the model has passed through every training set example (e.g., one epoch means every example has been processed once)

Your hyperparameters work together to create the model best fit for your data. Typically, during the machine learning training process, you'll require multiple epochs, and each epoch allows your model to improve by adjusting internal parameters to fit the data better. This is particularly common when training neural networks, where the model has an iterative learning process that improves by running through the training data several times.

Types of epoch training

How you divide your data into batches influences the frequency of model updates during an epoch, affecting training stability and convergence speed. Three methods you might use include:

Full batch gradient descent

In full batch gradient descent, your model processes all data as one batch for each epoch. When this is the case, your model updates its weights and biases one time per epoch. While computationally efficient and consistent, this method requires storing the entire training data set, which can slow the algorithm when working with large or complex models. 

If your data set contained 100,000 examples, your model would update once per epoch after evaluating all examples.

Stochastic gradient descent

When you choose a stochastic gradient descent, your model updates parameters after each piece of data. For example, if you have a data set of 100,000 examples, the model updates 100,000 times per epoch.

This method is often faster than full batch gradient descent and has a detailed rate of improvement. However, because of the frequent updates, this algorithm can be computationally expensive and more difficult converging on the best model. To help convergence, you may need to shuffle your data before each epoch of training to reduce bias in the model.

Mini-batch stochastic gradient descent

In a mini-batch stochastic gradient descent, your model updates after each batch size. This method is typically the go-to for most machine learning algorithms because it combines the concepts of the other two models to maximize robustness and efficiency. 

For example, if you had 100,000 training samples and a batch size of 1,000, you would have 100 batches per epoch. Updates happen after each batch, meaning your model would have 100 updates to the model per epoch. 

Who uses epochs?

Epochs are primarily used by professionals who work with machine learning, particularly deep learning models. You might find this type of work as a data scientist, machine learning engineer, big data engineer, natural language processing engineer, or business intelligence developer. 

Because epochs are a fundamental part of training machine learning models, the type of work you perform in your profession will dictate whether you use epochs rather than the title itself. If your work involves training machine learning models, especially those that require multiple iterations to learn complex relationships effectively, you will likely use epochs as part of your training process. These professionals use hyperparameters like epochs to optimize the learning process across a variety of fields and applications, including:

• Deep learning: You may use epochs to iteratively train neural networks for tasks that involve classifying media, making predictions, or extracting meaning from complex data. Iteratively training over several epochs helps to improve performance and model fit over time. 

• Natural language processing (NLP): You can use epochs to train models on text data, helping them understand language structure and word associations. Repeated training helps your model understand user input, produce new content, and generate more accurate responses. 

• Computer vision: In computer vision, epochs can be used to train models for image analysis tasks, such as object detection and image segmentation. Multiple passes through the data help improve the model's ability to recognize patterns, increasing performance accuracy. 

• Business analytics: In business analytics, you can use epochs in machine learning to train predictive models based on historical data. This helps you forecast trends, identify fraudulent activity, determine marketing needs, and predict stock prices. Choosing the right number of epochs can alter predictions, making it an important aspect of business analytics.

What are epochs used for?

The epoch count helps improve the machine learning training process in several ways. For one, it enables iterative learning by allowing repeated passes over the data, helping reduce errors while the model learns complex relationships between variables. It also allows continuous adjustment of model weights, updating parameters, and improving the model's fit. By influencing the training duration, the epoch count can prevent overfitting, which occurs when the algorithm has difficulty generalizing from training data to new data.

Determining the right epoch count: Benefits and challenges

The number of epochs you use during training will affect your model performance and the efficiency of your learning process. To decide on the right count, you'll need to find a balance between learning accuracy and efficiency. This is a nuanced process, and it takes time to find the appropriate number for best performance. Deciding to use a lower or higher epoch count each has its own set of advantages and disadvantages.

Low epoch count

With a low epoch count, you run the risk of underfitting, meaning the model doesn't learn enough to truly capture the underlying pattern of the data. This leads to poor generalization and higher error rates. However, with a smaller epoch count, you reduce the training time and, therefore, computational load, avoiding overfitting.

High epoch count

With a high epoch count, you can increase performance by providing more exposure to training data so that your model can find the appropriate parameter weights. However, larger epoch counts can increase runtime and may make the model too closely tailored to the training data, known as overfitting. This means the model has trouble accurately working with new data despite accurately fitting it to training data.

How to learn to use epochs

Learning to use epochs should be a part of a broader machine learning education. To start learning how to train machine learning models, follow these steps:

1. Take time to learn the fundamentals. Learning fundamental programming, statistics, mathematics, and data concepts can help you prepare to learn more advanced concepts.

2. Learn machine learning basics. Once you understand fundamentals, you can learn basic machine learning theory to help you understand how models function and troubleshoot when your model doesn't work how you expect. You can study independently or take structured courses like the Machine Learning Specialization on Coursera.

3. Complete practice projects. Hands-on practice is a great way to test what you've learned and identify areas for growth. You can find data sets online to work with or complete a Guided Project on Coursera in an area of interest.

Understand more machine learning concepts with Coursera. 

An epoch in machine learning is one complete pass through the training data set. Machine learning models increase their performance through multiple epochs and adjust their parameters to best fit the training data. 

While important, epochs are just one area of machine learning, and understanding the broader field is important to use epoch counts effectively. To learn more about machine learning models, consider taking exciting courses and Specializations on the Coursera learning platform. For intermediate learners, IBM offers Machine Learning with Python Specialization, or you can focus on Deep Learning with DeepLearning.AI.