Epoch

As explained in LLMs in Production

An epoch is one complete pass through a machine-learning training dataset.

An epoch is one complete pass through a machine-learning training dataset. Training processes the data in batches, calculates loss, and adjusts model weights repeatedly so predictions improve over time. section 5.2.1

Track an epoch alongside the practical risks of training:

• More epochs can improve performance on training data, but excessive training can cause overfitting on unseen data. section 5.2.1
• Use monitoring and techniques such as early stopping, dropout, or learning-rate scheduling to support generalizability; none is a guaranteed solution. section 5.2.1

Figure 5.2 A simplified version of all the steps necessary to train a language model (large or otherwise) from scratch. You must have data, then define all of the model behavior, and only then proceed to train.

Situates epochs within the broader sequence of preparing data, defining model behavior, and training a language model from scratch. section 5.2.1

How LLMs in Production covers it

How deep it goes

Defines an epoch through batches, loss, and weight adjustment while emphasizing monitoring rather than implementation details.

Examples

Uses language-model training from scratch and a pipeline figure showing data preparation, model definition, and training.

What you can do after

Evaluate training progress while balancing improvement on training data against generalization to unseen data.

How it connects

Connects epochs with batches, loss, model weights, overfitting, early stopping, dropout, and learning-rate scheduling.

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Also explained in

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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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  Inside Deep Learning: Math, Algorithms, ModelsOne complete pass over a dataset during training or evaluation.
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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 LLMs in Production. The section links point to the parts of the book it draws on.