liveBook · Manning

Fine-tuning

As explained in Machine Learning Algorithms in Depth

Fine-tuning is adapting a pretrained model to a target task by continuing training, typically with task-specific output parameters.

Fine-tuning adapts a pretrained model to a target task by continuing training, typically with task-specific output parameters. For BERT, a single additional output layer can support tasks without substantial architectural changes. section 11.5

BERT for task-specific prediction

A pretrained BERT model can be fine-tuned for question answering or language inference by adding one output layer rather than redesigning the architecture. section 11.5

How Machine Learning Algorithms in Depth covers it

How deep it goes
Defines task adaptation through an additional output layer but stops short of training mechanics, optimization, or implementation details.
Examples
Uses BERT-based question answering and language inference as concrete fine-tuning applications.
What you can do after
Apply pretrained BERT with an added output layer to multiple language-processing tasks.
How it connects
Connects pretrained models, transformer architectures, BERT, output layers, and zero-shot or few-shot learning.
Cover of Machine Learning Algorithms in Depth
liveBookmanning.com
Read Machine Learning Algorithms in Depth on liveBook Get the book at manning.com

Also explained in

Compare how 37 books explain fine-tuning →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.