Epoch (machine learning)

A full training pass over the entire training set such that each example has been processed once. An epoch represents N/batch size training iterations, where N is the total number of examples.¹

For instance, suppose the following:

The dataset consists of 1,000 examples.
The batch size is 50 examples.

Therefore, a single epoch requires 20 iterations. (1 epoch = 1,000 / 50 = 20)

Number of epochs

The number of epochs is a hyperparameter you set before the model begins training. In many cases, you’ll need to experiment with how many epochs it takes for the model to converge. In general, more epochs produces a better model, but also takes more time to train.²

Footnotes

ML crash course - Linear regression “When training a model, you'll often look at a loss curve to determine if the model has converged.”
mini-batch “For example, suppose the entire training set (the full batch) consists of 1,000 examples.”
역전파 “Training a neural network involves many iterations of the following two-pass cycle:”
Self-training (machine learning) “Use supervised learning to train a model on the labeled examples.”
Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]]
Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…”
Batch (machine learning) 이 글을 인용: “See epoch for an explanation of how a batch relates to an epoch.”
Generalization (machine learning) “To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data i…”
Clipping (machine learning) “For example, suppose that <0.5% of values for a particular feature fall outside the range 40–60.”
Learning rate “Learning rate is a key hyperparameter.”
Downsampling (machine learning) “For example, before training an image recognition model, downsampling high-resolution images to a lower-resolut…”
Training loss “A loss curve plots training loss versus the number of iterations.”
Parameter (machine learning) “For example, learning rate is a hyperparameter.”
지도 학습 “Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresp…”
Minority class (machine learning) “A training set with a million examples sounds impressive.”
모델 (기계학습) “In supervised machine learning, a model takes an example as input and infers a prediction as output.”
기계학습 “2023-12-27 - CI/CD for Machine Learning in 2024: Best Practices to Build, Test, and Deploy”
Semi-supervised learning “Self-training is one technique for semi-supervised learning.”
분류 모델 “Use as a rough indicator of model training progress/convergence for balanced datasets.”
Test set “For instance, a single example shouldn't belong to both the training set and the test set.”