November 2022
Beginner to intermediate
296 pages
8h 27m
English
Overview
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:
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
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).
Quotes
I loved this author’s detail, his command of linguistics, and how he brought all the concepts together.
- Vamsi Sistla, Nike
Full of non-trivial, real-world examples that expose the real potential of NLP.
- Rohit Agarwal, Mobisy Technologies
Covers deep learning for NLP from the most basic concepts to the-state-of-the-art Transformers architecture.
- Giuliano Araujo Bertoti, FATEC
Perfect for novice and expert practitioners alike.
- Ritwik Dubey, Mastercard
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.
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).
Quotes
I loved this author’s detail, his command of linguistics, and how he brought all the concepts together.
- Vamsi Sistla, Nike
Full of non-trivial, real-world examples that expose the real potential of NLP.
- Rohit Agarwal, Mobisy Technologies
Covers deep learning for NLP from the most basic concepts to the-state-of-the-art Transformers architecture.
- Giuliano Araujo Bertoti, FATEC
Perfect for novice and expert practitioners alike.
- Ritwik Dubey, Mastercard
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