Items related to Natural Language Processing with TensorFlow
Synopsis
Key Features
- Focus on natural language processing with TensorFlow, thereby avoiding the traditional focus on computer vision
- Treats NLP as a field in its own right, and learn to process and evaluate large unstructured data sets consisting of text
- Learn to apply the TensorFlow toolbox to the most interesting field in artificial intelligence
Book Description
TensorFlow is the most important deep learning framework currently in existence. Deep Learning algorithms are the most important frontier in artificial intelligence, with natural language processing (NLP) providing much of the engineering required to understand and process the vast majority of data available to deep learning applications today. Natural Language Processing with TensorFlow teaches aspiring deep learning developers to cope with unstructured data, that is, text and audio, which make up a large part of currently available data streams.
Thushan Ganegedara starts out by explaining the inner workings of TensorFlow itself, and the proceeds with a family of algorithms allowing sequences of words to be turned into vectors, making them accessible to deep learning algorithms in the process. He then shifts gears somewhat by showing how classical deep learning algorithms like convolutional neural networks (CNN) and recurrent neural (RNN) networks can be applied to NLP. Long short-term memory are a variety of RNNs, used in the next chapter for text generation.
Thushan concludes the book with an overview and implementation of neural machine translation, a method relying on deep learning algorithms to achieve impressive results in machine translation.
What you will learn
- How to master NLP based on existing TensorFlow algorithms
- Build NLP applications
- Write automatic translation programs using neural machine translation algorithms
- Use classical deep learning algorithms to classify sentences
- Apply Long Short-Term Memory to text generation
"synopsis" may belong to another edition of this title.
About the Author
Thushan is a seasoned ML practitioner with 4+ years of experience in the industry. Currently he is a senior machine learning engineer at Canva; an Australian startup that founded the online visual design software, Canva, serving millions of customers. His efforts are particularly concentrated in the search and recommendations group working on both visual and textual content. Prior to Canva, Thushan was a senior data scientist at QBE Insurance; an Australian Insurance company. Thushan was developing ML solutions for use-cases related to insurance claims. He also led efforts in developing a Speech2Text pipeline there. He obtained his PhD specializing in machine learning from the University of Sydney in 2018.
"About this title" may belong to another edition of this title.
- PublisherPackt Publishing - ebooks Account
- Publication date2018
- ISBN 10 1788478312
- ISBN 13 9781788478311
- BindingPaperback
- LanguageEnglish
- Number of pages350
- Rating
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Natural Language Processing with TensorFlow
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Write modern natural language processing applications using deep learning algorithms and TensorFlow Key Features:Focuses on more efficient natural language processing using TensorFlow Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches Provides choices for how to process and evaluate large unstructured text datasets Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence Book Description: Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today's data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. What You Will Learn:Core concepts of NLP and various approaches to natural language processing How to solve NLP tasks by applying TensorFlow functions to create neural networks Strategies to process large amounts of data into word representations that can be used by deep learning applications Techniques for performing sentence classification and language generation using CNNs and RNNs About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks How to write automatic translation programs and implement an actual neural machine translator from scratch The trends and innovations that are paving the future in NLP Who this book is for: This book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful. Seller Inventory # 9781788478311
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