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Item #:
46480568
Deep Learning for Natural Language Processing
78% of respondents would recommend this to a friend
Item #:
46480568
XPF 6708
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Deep learning has transformed the field of natural language processing.
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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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Customer Questions & Answers
-
Question:
What is the book about?
Answer: The book explores deep learning techniques for natural language processing and teaches readers how to create advanced NLP applications using Python and Keras. -
Question:
Who is the author?
Answer: The author is Stephan Raaijmakers, a professor of Communicative AI and a senior scientist at The Netherlands Organization for Applied Scientific Research (TNO). -
Question:
What level of programming and NLP knowledge is required?
Answer: Readers should have intermediate Python skills and a general knowledge of NLP. No experience with deep learning is required.
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.
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XPF 6708
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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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