Hands on Question Answering Systems (16 results)

Paperback or Softback. Condition: New. Hands-On Question Answering Systems with Bert: Applications in Neural Networks and Natural Language Processing. Book.

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Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Condition: New. 2021. 1st ed. paperback. . . . . .

Paperback. Condition: Brand New. 184 pages. 9.50x6.25x0.50 inches. In Stock.

Condition: New. 2021. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Condition: New. In.

PF. Condition: New.

Condition: New. 1st ed. edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Neuware -Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, yoüll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, yoüll cover word embedding and their types along with the basics of BERT.After this solid foundation, yoüll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. Yoüll see different BERT variations followed by a hands-on example of a question answering system.Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.What You Will LearnExamine the fundamentals of word embeddingsApply neural networks and BERT for various NLP tasksDevelop a question-answering system from scratchTrain question-answering systems for your own dataWho This Book Is ForAI and machine learning developers and natural language processing developers.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 200 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.What You Will Learn Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is ForAI and machine learning developers and natural language processing developers. 200 pp. Englisch.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Integrates question answering systems with document repositories from different sourcesContains an in-depth explanation of the technology behind BERTTakes a step-by-step approach to building ques.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.What You Will Learn Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is ForAI and machine learning developers and natural language processing developers.