Transfer learning

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover transfer learning?

Defines adaptation and limited additional effort, but stops short of describing fine-tuning procedures or implementation details.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

A technique that adapts knowledge learned from one task or dataset to a related task.

Transfer learning adapts a deep-learning network trained for one problem to a similar task with limited additional modeling effort. It is especially established for images and text, where reusable pretrained models are widely available. section 1.4

Tabular data makes this strategy harder to generalize: datasets vary widely, and broadly reusable pretrained solutions are difficult to obtain. Consequently, feature engineering often remains more influential for tabular machine learning. section 1.4

A practical contrast

A network trained on one image or text problem can be tweaked for a related task. A tabular-data model has fewer ready-made starting points because tabular problems differ substantially in structure and information. section 1.4

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

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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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Natural Language Processing in Action, Second EditionTransfer learning is reusing knowledge learned from one task or dataset to improve learning on another task or dataset.
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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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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 Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.