Epoch

As explained in Generative AI in Action

An epoch is one complete pass through the examples in a training dataset during model training.

An epoch is one complete pass through every example in a training dataset. Fine-tuning repeats this process so the model can update its parameters, while validation data helps reveal whether additional passes improve generalization or encourage overfitting. section 9.3.1 section 9.3.4

Use the epoch count as one training control alongside dataset quality and other hyperparameters:

• Increase or reduce the number of epochs while monitoring validation performance; training loss alone can hide overfitting. section 9.3.1 section 9.3.4
• Interpret a good fit as training and validation loss decreasing toward stability with only a small gap between them. section 9.3.4
• Treat the epoch count as a production trade-off: insufficient training can underfit, whereas excessive training can reduce performance on unseen data. section 9.3.4

Setting epochs for an Azure OpenAI fine-tuning job

The fine-tuning request sets n_epochs to 3 while selecting GPT-3.5 Turbo 0613. The job is queued, so its status should be tracked until it succeeds or fails. section 9.3.3

Figure 9.3 Training loss when fine-tuning GPT-3.5 Turbo

Inspect the training-loss figure to connect epoch-based evaluation with changes in model performance during fine-tuning. section 9.3.4

How Generative AI in Action covers it

How deep it goes

Defines a complete dataset pass and relates epoch count to loss, overfitting, underfitting, and validation, without deriving optimization mathematics.

Examples

Uses Azure OpenAI fine-tuning with n_epochs set to 3 and interprets training and validation loss curves.

What it assumes

Assumes familiarity with training and validation datasets, while defining an epoch through its relationship to dataset passes and model updates.

What you can do after

Operate/configure fine-tuning by selecting an epoch count and diagnose generalization through training and validation loss.

How it connects

Connects training datasets, validation datasets, hyperparameters, training loss, validation loss, overfitting, and underfitting.

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Also explained in

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  Grokking Machine LearningOne iteration of a training loop that applies an update procedure to a selected training example.
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  Software Testing with Generative AIAn epoch is one complete iteration through all entries in a training dataset during model training.
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  Learn Generative AI with PyTorchOne complete pass through the entire training dataset during model training.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksAn epoch is one iteration or pass of a machine-learning training procedure over its training process or data.
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  Distributed Machine Learning PatternsAn epoch is one complete pass through every training example in a dataset during model training.
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  Machine Learning Platform EngineeringAn epoch is one complete pass through the training dataset during model training.
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  Inside Deep Learning: Math, Algorithms, ModelsOne complete pass over a dataset during training or evaluation.
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  LLMs in ProductionAn epoch is one complete pass through a machine-learning training dataset.
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  Transformers in ActionA training epoch is one complete pass through the training dataset during model optimization.

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Related concepts

• trainingpart of · Training is a process of developing or improving a person’s or model’s capabilities through instruction, experience, or adjustment using data.
• training datasetuses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
• fine-tuningconfigures · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• training looppart of · A training loop is an iterative procedure that processes training data, evaluates a model objective, and updates model parameters to improve performance.
• time_pointused by · std::chrono::time_point is a C++ standard library class template representing a point in time relative to a clock's epoch, measured using a duration.
• hyperparameter tuningused by · Hyperparameter tuning is the process of selecting or adjusting configurable model or procedure settings and evaluating them against a chosen objective.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.