Transfer learning

As explained in Natural Language Processing in Action, Second Edition

Transfer learning is reusing knowledge learned from one task or dataset to improve learning on another task or dataset.

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Explains initialization, vocabulary alignment, missing-token handling, fine-tuning, validation, and evaluation, but not pretrained-model training internals.

Examples

Uses a 4,000-token GloVe matrix and a toxic-comment BERT classifier evaluated with LRAP, accuracy, and evaluation loss.

What it assumes

Assumes tokenized vocabularies, labeled datasets, train-validation splits, and basic Python libraries while demonstrating model setup.

What you can do after

Builds transfer-learning pipelines with PyTorch embeddings or fine-tunes pretrained BERT models for labeled text classification.

How it connects

Connects pretrained embeddings, GloVe, CNNs, BERT, fine-tuning, multilabel classification, and out-of-vocabulary tokens.

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Also explained in

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  Inside Deep Learning: Math, Algorithms, ModelsA training approach that adapts a pretrained model to a new task, usually by reusing learned weights and matching the original preprocessing expectations.
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  LLMs in ProductionTransfer learning is a machine-learning technique that adapts knowledge learned from one task or dataset to another task or domain.
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  Generative AI in ActionTransfer learning adapts knowledge learned from one task or dataset to improve performance on another task.
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  Knowledge Graphs and LLMs in ActionTransfer learning is a machine-learning technique that reuses patterns learned from one task or domain to improve performance on another task or domain.
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  Time Series Forecasting Using Foundation ModelsTransfer learning is a technique that applies knowledge learned by a model on one dataset or task to a different dataset or task.
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  Grokking Machine LearningTransfer learning is a technique that adapts a pretrained model to a new task or dataset by continuing its training, often by modifying only some parameters.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA technique that adapts knowledge learned from one task or dataset to a related task.
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  Sutskever's ListTransfer learning is a technique that adapts knowledge learned from one task or dataset to another task or dataset.
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  Domain-Specific Small Language ModelsThe process of taking a model trained on one task or domain and applying it to a related task or domain, often with further tuning.
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  Outlier Detection in PythonTransfer learning is a technique that adapts knowledge learned by a model on one task or dataset to another task or dataset.

Compare how 11 books explain transfer learning →

Related concepts

• fine-tuningused by · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• pretrained modeluses · A pretrained model is a machine-learning model whose parameters were learned through prior training before it is used or adapted for a subsequent task.
• embeddinguses · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
• generalizationuses · Generalization is the extension of a pattern, model, solution, or conclusion beyond the specific examples or cases from which it was derived.
• unlabeled datauses · Unlabeled data consists of examples that lack target labels or task-specific annotations.
• causal invarianceused by · Causal invariance is the stability of a causal relationship across changes in environments or data distributions.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.