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Item #:
46480568
Deep Learning for Natural Language Processing
78% of respondents would recommend this to a friend
Item #:
46480568
NZD 99
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What Stands Out
Advanced Algorithms
Utilizes cutting-edge algorithms to enhance text understanding, enabling more accurate interpretations and responses in NLP applications, setting it apart from traditional methods.
Real-World Applications
Designed for practical implementations across various industries such as healthcare, finance, and customer service, addressing diverse user needs and streamline workflows effectively.
Comprehensive Resources
Offers extensive resources, including code examples and tutorials, empowering users with tools to master deep learning techniques for NLP, ensuring they can implement solutions confidently.
Product Details
- Explores the challenging issues of natural language processing and provides solutions using cutting-edge deep learning
- Covers topics such as NLP overview, one-hot text representations, word embeddings, and models for textual similarity
- Discusses sequential NLP, semantic role labeling, deep memory-based NLP, and linguistic structure
- Provides insights on hyperparameters for deep NLP and the application of deep learning in NLP
- Teaches how to create advanced NLP applications using Python and the Keras deep learning library
- Includes real-world examples and detailed code discussions for practical learning purposes
| Publisher | Manning |
| Publication date | December 6, 2022 |
| Edition | First Edition |
| Language | English |
| Print length | 296 pages |
| ISBN-10 | 1617295442 |
| ISBN-13 | 978-1617295447 |
| Item Weight | 1 pounds (450 grams) |
| Dimensions | 7.38 x 0.7 x 9.25 inches (18.7 x 1.8 x 23.5 cm) |
Product Description
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Product Buying Guide
Deep Learning for Natural Language Processing First Edition is a comprehensive guide that explores the most challenging issues of natural language processing and teaches readers how to solve them using cutting-edge deep learning techniques. This buying guide provides all the essential information potential buyers need to make an informed decision.
Product Specifications
- Author: Stephan Raaijmakers
- Publisher: Manning Publications
- Edition: First
- Format: Print book, eBook (PDF, Kindle, ePub)
- Language: English
- Number of pages: Varies
- Publication date: Varies
- Target audience: Readers with intermediate Python skills and a general knowledge of NLP
Key Features
- 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
- In-depth discussion of BERT, XLNET, and other techniques
- Real-world applications and examples
- Code discussions and adaptation
Usage Scenarios
- Creating advanced NLP applications using Python and Keras
- Improving question answering with sequential NLP
- Boosting performance with linguistic multitask learning
- Accurately interpreting linguistic structure
- Mastering multiple word embedding techniques
- Developing deep learning-based NLP models
Usage Scenarios
- Natural Language Processing with Python by Bird, Klein, and Loper
- Speech and Language Processing by Jurafsky and Martin
- Neural Networks for Natural Language Processing by Goldberg
Some User Review
- The book provides a comprehensive and practical approach to deep learning for NLP. The examples and code discussions are really helpful.
- I found the chapter on Transformers and BERT particularly insightful. The hands-on examples helped me understand their applications better.
- As someone with intermediate Python skills, I found this book to be the perfect balance of theory and practical implementation. Highly recommended!
Competitors
- The price of the book varies depending on the format and the retailer. It is important to compare prices from different sellers to find the best deal.
- Considering the valuable insights and practical knowledge provided in the book, the price is reasonable and worth the investment for anyone interested in deep learning for NLP.
Buying Considerations
- Evaluate your current Python skills and NLP knowledge to determine if this book is suitable for your level of expertise.
- Consider if you prefer a print book or an eBook in PDF, Kindle, or ePub format.
- Check the publication date and edition to ensure you are purchasing the most up-to-date version.
- Compare prices from different retailers to find the best deal.
- Read user reviews and consider the feedback from others who have already benefited from this book.
Conclusion
Deep Learning for Natural Language Processing First Edition is a must-have guide for anyone interested in exploring and implementing deep learning techniques in the field of natural language processing. With its comprehensive coverage, real-world examples, and practical code discussions, this book will help readers enhance their NLP skills and develop powerful applications.
View LessDeep Learning for Natural Language Processing First Edition is a comprehensive guide that explores the most challenging issues of natural language processing and teaches readers how to solve them using cutting-edge deep learning techniques. This buying guide provides all the essential information potential buyers need to make an informed decision. Continue Reading
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Intelligence & Semantics Editorial Review
**** "Deep Learning for Natural Language Processing" aims to bridge the gap in understanding deep learning concepts as applied to natural language processing (NLP). The book introduces fundamental ideas clearly, including topics like attention mechanisms and sequential models, which many readers found beneficial for building a solid foundational knowledge in the domain. However, despite its ambitions, the book has drawn significant criticism for several critical shortcomings. One of the most glaring issues highlighted by readers is the lack of associated datasets and a GitHub repository. This absence makes it challenging for learners to directly apply the concepts and follow along with the examples provided in the text. Many have expressed frustration over attempting to execute the provided code, which often fails due to missing datasets and outdated snippets. Key code snippets reportedly contain various errors due to version discrepancies, leading to a disjointed learning experience for readers trying to replicate the examples. Additionally, the quality of the code has been called into question. Readers noted numerous typos, poor indentation, and coding practices not conforming to Python's PEP-8 standards. Users who are already versed in Python found these flaws particularly disappointing, while newcomers may inadvertently learn poor coding practices as a result. Given the competitive nature of educational materials in this space, many reviewers suggested that the book falls short in both the depth of its content and the quality of its supporting code. In summary, while "Deep Learning for Natural Language Processing" manages to touch upon important concepts in deep learning and NLP, the execution regarding code quality, practical application, and depth of exploration has left many readers disenchanted. Potential buyers seeking a more robust learning resource are advised to Consider alternative options. **
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Pros
- Clear introduction to fundamental deep learning concepts for NLP (like attention and sequential models).
- Unique coverage of topics such as multi-task learning in the NLP context.
Cons
- Lack of associated datasets and GitHub repository for practical engagement.
Product Price History
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NZD 99
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Features & Benefits
- Deep learning has revolutionized NLP
- Computer systems can achieve human levels of comprehension and context
- Powerful deep learning-based NLP models open up potential uses
- Teaches how to create advanced NLP applications using Python and Keras
- Includes examples and code discussions for hands-on experience
- For readers with intermediate Python skills and general NLP knowledge
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