Overfitting

As explained in Learn Generative AI with PyTorch

A training failure mode where the model memorizes training data too closely and generalizes poorly to new inputs.

Overfitting occurs when a model performs well on training data but poorly on unseen inputs. The model has learned the training examples too closely instead of generalizing. section 2.2.1 section 2.4.1

Training loss alone cannot identify the best model. A validation set supports hyperparameter selection and reveals when additional training stops improving generalization. section 2.2.1 section 2.4.1

A practical training workflow uses separate data and a stopping rule:

• Divide the original training data into a training set and a validation set; one example uses 50,000 and 10,000 observations. section 2.4.1
• Load training, validation, and test sets separately with PyTorch DataLoader objects. section 2.4.1
• Track validation loss while training and stop after it fails to improve for a chosen patience period; the example uses patience=10. section 2.4.1
• Evaluate the trained model on the test set after selecting when to stop. section 2.2.1

A GPT model memorizing passages

A decoder-only GPT model is trained to predict the next token. Training for too long can make its generated text identical to passages in the novels used for training. Comparing checkpoints saved every 10 epochs shows the tradeoff: the 40-epoch model is highly coherent but may copy passages, while the 20-epoch model is less likely to copy but can contain grammatical errors. section 12.1.2 section 12.0

Figure 2.1 The steps involved in training a deep learning model

Use the training-process figure to follow prediction, loss calculation, backpropagation, and weight updates as the model learns. section 2.2.1

How Learn Generative AI with PyTorch covers it

How deep it goes

Defines the failure mode, demonstrates its effects, and gives validation-loss early stopping, but does not develop broader regularization methods.

Examples

Demonstrates image classification with train-validation-test splits and GPT text generation that copies training passages.

What it assumes

Assumes basic model training and loss concepts while introducing validation splits, epochs, hyperparameters, and test evaluation.

What you can do after

Diagnose overfitting with validation loss and control it by selecting checkpoints or stopping training after a patience interval.

How it connects

Connects overfitting to validation sets, hyperparameters, early stopping, training loss, test sets, and cross-entropy loss.

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  Grokking Machine LearningOverfitting is a modeling condition in which a model captures noise or idiosyncrasies in training data and generalizes poorly to new data.
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  Deep Learning with Python, Third EditionThe failure mode where performance improves on training data while degrading on unseen data because the model has adapted too specifically to the training set.
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  Deep Learning with R, Third EditionOverfitting is a modeling condition in which performance is better on training data than on previously unseen data, including when repeated tuning adapts a model to validation data.
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  Deep Learning with PyTorch, Second EditionA failure mode where a model memorizes irrelevant details of the training data and generalizes poorly, discussed here as a risk of increased capacity.
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  Causal Inference for Data ScienceOverfitting is the condition in which a model captures noise or idiosyncrasies in training data and consequently generalizes poorly to new data.
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  Natural Language Processing in Action, Second EditionOverfitting is a modeling failure in which a model relies too heavily on peculiarities of its training data and performs poorly on new data.
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  Math and Architectures of Deep LearningA training failure mode in which a model fits idiosyncrasies of the training data too closely and generalizes poorly.
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  Grokking AI Algorithms, Second EditionOverfitting is a condition in which a model matches training data too closely and performs poorly on varied unseen data.
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  Ensemble Methods for Machine LearningOverfitting is a modeling failure in which a model fits training data too closely and does not necessarily generalize well to new data.
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  The AI Pocket BookA failure mode in which a model becomes too specialized to its training data and generalizes poorly to new cases.
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  Data Without LabelsOverfitting is a modeling condition in which a supervised learning model performs well on training data but less accurately on unseen data.
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  Investing for ProgrammersOverfitting is a modeling failure in which a model captures irrelevant variation in training features and fails to generalize reliably to new data.
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  Generative AI in ActionOverfitting is a modeling condition in which learned patterns fit training data too closely and fail to generalize well to new examples.
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  Machine Learning System DesignA condition in which a model fits its training data too closely and performs poorly on unseen data because it captures insignificant or non-generalizable patterns.
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  Starting Data Analytics with Generative AI and PythonOverfitting is a modeling failure in which a model fits noise or sample-specific variation rather than generalizable underlying patterns.
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  Demand Forecasting Best PracticesOverfitting is a modeling condition in which a model fits historical data well but fails to generalize to future or otherwise unseen data.
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  Sutskever's ListOverfitting is a modeling failure in which a model fits training data too closely and performs poorly on unseen data.
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  Statistics Every Programmer NeedsOverfitting is a modeling condition in which a model captures idiosyncrasies of the fitting data at the expense of performance on new data.
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  LLMs in ProductionOverfitting is a condition in which a model becomes excessively tailored to training data and performs poorly on unseen data.
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  Graph Neural Networks in ActionA modeling failure mode in which a model fits training data patterns too closely and generalizes poorly to validation or unseen data.
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  Reinforcement Learning from Human FeedbackOverfitting is a training condition in which a model fits its training data or objective too closely and generalizes poorly.
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  Building Reliable AI SystemsA model-training outcome in which a model fits training examples too closely and generalizes poorly to new data.
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  Financial AI in PracticeOverfitting occurs when a model captures patterns specific to its training data that do not generalize reliably to new data.

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Related concepts

• regularizationaddressed by · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
• dropoutaddressed by · Dropout is a neural-network regularization technique that randomly suppresses units, connections, or activations during training to reduce overfitting.
• early stoppingaddressed by · Early stopping is a technique that terminates an iterative process before its planned completion when a specified condition indicates that further work is unnecessary or unlikely to improve the objective.
• underfittingcontrasts with · A modeling failure in which insufficient capacity or training prevents a model from capturing relevant patterns in the data, resulting in poor performance even on training data.
• failure modekind of · A failure mode is a distinct way in which a system, component, process, or operation can fail or produce an undesirable result.
• tree pruningaddressed by · Tree pruning is the removal of decision-tree branches that fail a specified split-quality condition.

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