Transfer Learning for Natural Language Processing is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models. Written by DARPA researcher Paul Azunre, this practical book gets you up to speed with the relevant ML concepts before diving into the cutting-edge advances that are defining the future of NLP. You’ll learn how to adapt existing state-of-the art models into real-world applications, including building a spam email classifier, a movie review sentiment analyzer, an automated fact checker, a question-answering system and a translation system for low-resource languages. For each NLP application, you’ll learn how to setup a microservices software architecture that will fine-tune your model as new data comes in. Learn more about the book here: https://www.manning.com/books/transfer-learning-for-natural-language-processing

1. Improving NLP Model
Training Time and
Efficiency
With Transfer Learning for Natural
Language Processing. Take 42% off by
entering slazunre into the discount code
box at checkout at manning.com.

2. Building and training deep
learning models from
scratch is costly, time-
consuming, and requires
massive amounts of data.
To address this concern,
cutting-edge transfer
learning techniques enable
you to start with pretrained
models you can tweak to
meet your exact needs.

3. Transfer Learning for Natural Language Processing shows you how to
customize open source resources for your own NLP architectures.
You’ll learn how to use transfer learning to deliver state-of-the-art
results even when working with limited label data, all while saving on
training time and computational costs.

4. Transfer Learning for Natural
Language Processing is a
practical primer to transfer
learning techniques capable of
delivering huge improvements
to your NLP models.
For several NLP applications,
you’ll learn to setup a
software architecture that will
fine-tune your model as new
data comes in.

5. What people are saying
about the book:
An interesting book
that introduces
Transfer Learning
techniques in the
domain of NLP.
-Nikos Kanakaris
A complex topic is broken
down into manageable pieces,
while maintaining a good
pace. The text is accompanied
by replicable code examples
throughout.
-Mathijs Affourtit

6. About the author:
Paul Azunre holds a PhD in
Computer Science from MIT. He
works as a Research Director
studying Transfer Learning in NLP.
He is active in the field writing peer-
reviewed articles, serving as a
program committee member and
reviewing for top conferences. Paul
has also served as a Principal
Investigator on several DARPA
research programs at the US
Department of Defense.

7. Take 42% off Transfer Learning for
Natural Language Processing by entering
slazunre into the discount code box at
checkout at manning.com.
You can check the book our on our
browser-based liveBook reader here.