Synopsis
Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning!
Inside Deep Learning for Natural Language Processing you’ll find a wealth of NLP insights, including:
An overview of NLP and deep learning
One-hot text representations
Word embeddings
Models for textual similarity
Sequential NLP
Semantic role labeling
Deep memory-based NLP
Linguistic structure
Hyperparameters for deep NLP
Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses.
About the book
Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You’ll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses!
What's inside
Improve question answering with sequential NLP
Boost performance with linguistic multitask learning
Accurately interpret linguistic structure
Master multiple word embedding techniques
About the reader
For readers with intermediate Python skills and a general knowledge of NLP. No experience with deep learning is required.
About the author
Stephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist at The Netherlands Organization for Applied Scientific Research (TNO).
Table of Contents
PART 1 INTRODUCTION
1 Deep learning for NLP
2 Deep learning and language: The basics
3 Text embeddings
PART 2 DEEP NLP
4 Textual similarity
5 Sequential NLP
6 Episodic memory for NLP
PART 3 ADVANCED TOPICS
7 Attention
8 Multitask learning
9 Transformers
10 Applications of Transformers: Hands-on with BERT
"synopsis" may belong to another edition of this title.
About the Author
Stephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist (machine learning, NLP) at TNO.
"About this title" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning Models for textual similarity Deep memory-based NLP Semantic role labeling Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. Hes the technical coordinator of two large European Union-funded research security-related projects. Hes currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781617295447