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ISBN10: 1617295442
ISBN13: 9781617295447
Publisher: Manning Publications
Published: Dec 6 2022
Pages: 296
Weight: 1.01
Height: 0.65 Width: 7.41 Depth: 9.26
Language: English
ISBN13: 9781617295447
Publisher: Manning Publications
Published: Dec 6 2022
Pages: 296
Weight: 1.01
Height: 0.65 Width: 7.41 Depth: 9.26
Language: English
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
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. 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
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. 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
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