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Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Write modern natural language processing applications using deep learning algorithms and TensorFlow
Key FeaturesFocuses on more efficient natural language processing using TensorFlowCovers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approachesProvides choices for how to process and evaluate large unstructured text datasetsLearn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligenceBook DescriptionNatural 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 learnCore concepts of NLP and various approaches to natural language processingHow to solve NLP tasks by applying TensorFlow functions to create neural networksStrategies to process large amounts of data into word representations that can be used by deep learning applicationsTechniques for performing sentence classification and language generation using CNNs and RNNsAbout employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasksHow to write automatic translation programs and implement an actual neural machine translator from scratchThe trends and innovations that are paving the future in NLPWho This Book Is ForThis 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.
Table of ContentsIntroductionHow to Get TensorFlow to WorkProducing Word Embeddings with Word2VecAdvanced Word2VecSentence Classification with CNNsLanguage Modelling with RNNsWhat is LSTM?Applying LSTM to Text GenerationApplications of Image Caption GenerationNeural Machine TranslationNLP developments and TrendsAppendix I Linear Algebra and Stat
Key FeaturesFocuses on more efficient natural language processing using TensorFlowCovers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approachesProvides choices for how to process and evaluate large unstructured text datasetsLearn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligenceBook DescriptionNatural 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 learnCore concepts of NLP and various approaches to natural language processingHow to solve NLP tasks by applying TensorFlow functions to create neural networksStrategies to process large amounts of data into word representations that can be used by deep learning applicationsTechniques for performing sentence classification and language generation using CNNs and RNNsAbout employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasksHow to write automatic translation programs and implement an actual neural machine translator from scratchThe trends and innovations that are paving the future in NLPWho This Book Is ForThis 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.
Table of ContentsIntroductionHow to Get TensorFlow to WorkProducing Word Embeddings with Word2VecAdvanced Word2VecSentence Classification with CNNsLanguage Modelling with RNNsWhat is LSTM?Applying LSTM to Text GenerationApplications of Image Caption GenerationNeural Machine TranslationNLP developments and TrendsAppendix I Linear Algebra and Stat
774 pages, Kindle Edition
Published May 31, 2018
20 people are currently reading
46 people want to read
About the author
Thushan Ganegedara
4 books3 followersThushan Ganegedara has completed his Ph.D. at the University of Sydney, Australia. He is specializing in machine learning and has a liking for deep learning. He lives dangerously and runs algorithms on untested data. He also works as the chief data scientist for AssessThreat, an Australian start-up. He got his BSc. (Hons) from the University of Moratuwa, Sri Lanka. He frequently writes technical articles and tutorials about machine learning. Additionally, he also strives for a healthy lifestyle by including swimming in his daily schedule.
🏅Top AI Writer | ML @ Canva | PhD👨🏾🎓.
Educator (Manning/Packt/DataCamp)
Youtube: @DeepLearningHero
LinkedIN: thushan.ganegedara
https://thushv89.medium.com/membership
🏅Top AI Writer | ML @ Canva | PhD👨🏾🎓.
Educator (Manning/Packt/DataCamp)
Youtube: @DeepLearningHero
LinkedIN: thushan.ganegedara
https://thushv89.medium.com/membership
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