Natural language processing with TensorFlow :: teach language to machines using Python's deep learning library /
TensorFlow is the leading framework for deep learning algorithms critical to artificial intelligence, and natural language processing (NLP) makes much of the data used by deep learning applications accessible to them. This book brings the two together and teaches deep learning developers how to work...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt,
[2018]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | TensorFlow is the leading framework for deep learning algorithms critical to artificial intelligence, and natural language processing (NLP) makes much of the data used by deep learning applications accessible to them. This book brings the two together and teaches deep learning developers how to work with today's vast amount of unstructured data. |
| Beschreibung: | Implementing subsampling. |
| Beschreibung: | 1 online resource (472 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781788477758 1788477758 |
Internformat
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| 245 | 1 | 0 | |a Natural language processing with TensorFlow : |b teach language to machines using Python's deep learning library / |c Thushan Ganegedara. |
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| 505 | 0 | |a Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Natural Language Processing; What is Natural Language Processing?; Tasks of Natural Language Processing; The traditional approach to Natural Language Processing; Understanding the traditional approach; Example -- generating football game summaries; Drawbacks of the traditional approach; The deep learning approach to Natural Language Processing; History of deep learning; The current state of deep learning and NLP; Understanding a simple deep model -- a Fully Connected Neural Network. | |
| 505 | 8 | |a The roadmap -- beyond this chapterIntroduction to the technical tools; Description of the tools; Installing Python and scikit-learn; Installing Jupyter Notebook; Installing TensorFlow; Summary; Chapter 2: Understanding TensorFlow; What is TensorFlow?; Getting started with TensorFlow; TensorFlow client in detail; TensorFlow architecture -- what happens when you execute the client?; Cafe Le TensorFlow -- understanding TensorFlow with an analogy; Inputs, variables, outputs, and operations; Defining inputs in TensorFlow; Feeding data with Python code; Preloading and storing data as tensors. | |
| 505 | 8 | |a Building an input pipelineDefining variables in TensorFlow; Defining TensorFlow outputs; Defining TensorFlow operations; Comparison operations; Mathematical operations; Scatter and gather operations; Neural network-related operations; Reusing variables with scoping; Implementing our first neural network; Preparing the data; Defining the TensorFlow graph; Running the neural network; Summary; Chapter 3: Word2vec -- Learning Word Embeddings; What is a word representation or meaning?; Classical approaches to learning word representation. | |
| 505 | 8 | |a WordNet -- using an external lexical knowledge base for learning word representationsTour of WordNet; Problems with WordNet; One-hot encoded representation; The TF-IDF method; Co-occurrence matrix; Word2vec -- a neural network-based approach to learning word representation; Exercise: is queen = king -- he + she?; Designing a loss function for learning word embeddings; The skip-gram algorithm; From raw text to structured data; Learning the word embeddings with a neural network; Formulating a practical loss function; Efficiently approximating the loss function. | |
| 505 | 8 | |a Implementing skip-gram with TensorFlowThe Continuous Bag-of-Words algorithm; Implementing CBOW in TensorFlow; Summary; Chapter 4: Advanced Word2vec; The original skip-gram algorithm; Implementing the original skip-gram algorithm; Comparing the original skip-gram with the improved skip-gram; Comparing skip-gram with CBOW; Performance comparison; Which is the winner, skip-gram or CBOW?; Extensions to the word embeddings algorithms; Using the unigram distribution for negative sampling; Implementing unigram-based negative sampling; Subsampling -- probabilistically ignoring the common words. | |
| 500 | |a Implementing subsampling. | ||
| 520 | |a TensorFlow is the leading framework for deep learning algorithms critical to artificial intelligence, and natural language processing (NLP) makes much of the data used by deep learning applications accessible to them. This book brings the two together and teaches deep learning developers how to work with today's vast amount of unstructured data. | ||
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| contents | Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Natural Language Processing; What is Natural Language Processing?; Tasks of Natural Language Processing; The traditional approach to Natural Language Processing; Understanding the traditional approach; Example -- generating football game summaries; Drawbacks of the traditional approach; The deep learning approach to Natural Language Processing; History of deep learning; The current state of deep learning and NLP; Understanding a simple deep model -- a Fully Connected Neural Network. The roadmap -- beyond this chapterIntroduction to the technical tools; Description of the tools; Installing Python and scikit-learn; Installing Jupyter Notebook; Installing TensorFlow; Summary; Chapter 2: Understanding TensorFlow; What is TensorFlow?; Getting started with TensorFlow; TensorFlow client in detail; TensorFlow architecture -- what happens when you execute the client?; Cafe Le TensorFlow -- understanding TensorFlow with an analogy; Inputs, variables, outputs, and operations; Defining inputs in TensorFlow; Feeding data with Python code; Preloading and storing data as tensors. Building an input pipelineDefining variables in TensorFlow; Defining TensorFlow outputs; Defining TensorFlow operations; Comparison operations; Mathematical operations; Scatter and gather operations; Neural network-related operations; Reusing variables with scoping; Implementing our first neural network; Preparing the data; Defining the TensorFlow graph; Running the neural network; Summary; Chapter 3: Word2vec -- Learning Word Embeddings; What is a word representation or meaning?; Classical approaches to learning word representation. WordNet -- using an external lexical knowledge base for learning word representationsTour of WordNet; Problems with WordNet; One-hot encoded representation; The TF-IDF method; Co-occurrence matrix; Word2vec -- a neural network-based approach to learning word representation; Exercise: is queen = king -- he + she?; Designing a loss function for learning word embeddings; The skip-gram algorithm; From raw text to structured data; Learning the word embeddings with a neural network; Formulating a practical loss function; Efficiently approximating the loss function. Implementing skip-gram with TensorFlowThe Continuous Bag-of-Words algorithm; Implementing CBOW in TensorFlow; Summary; Chapter 4: Advanced Word2vec; The original skip-gram algorithm; Implementing the original skip-gram algorithm; Comparing the original skip-gram with the improved skip-gram; Comparing skip-gram with CBOW; Performance comparison; Which is the winner, skip-gram or CBOW?; Extensions to the word embeddings algorithms; Using the unigram distribution for negative sampling; Implementing unigram-based negative sampling; Subsampling -- probabilistically ignoring the common words. |
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| publisher | Packt, |
| record_format | marc |
| spelling | Ganegedara, Thushan, author. Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / Thushan Ganegedara. Birmingham, UK : Packt, [2018] ©2018 1 online resource (472 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Print version record. Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Natural Language Processing; What is Natural Language Processing?; Tasks of Natural Language Processing; The traditional approach to Natural Language Processing; Understanding the traditional approach; Example -- generating football game summaries; Drawbacks of the traditional approach; The deep learning approach to Natural Language Processing; History of deep learning; The current state of deep learning and NLP; Understanding a simple deep model -- a Fully Connected Neural Network. The roadmap -- beyond this chapterIntroduction to the technical tools; Description of the tools; Installing Python and scikit-learn; Installing Jupyter Notebook; Installing TensorFlow; Summary; Chapter 2: Understanding TensorFlow; What is TensorFlow?; Getting started with TensorFlow; TensorFlow client in detail; TensorFlow architecture -- what happens when you execute the client?; Cafe Le TensorFlow -- understanding TensorFlow with an analogy; Inputs, variables, outputs, and operations; Defining inputs in TensorFlow; Feeding data with Python code; Preloading and storing data as tensors. Building an input pipelineDefining variables in TensorFlow; Defining TensorFlow outputs; Defining TensorFlow operations; Comparison operations; Mathematical operations; Scatter and gather operations; Neural network-related operations; Reusing variables with scoping; Implementing our first neural network; Preparing the data; Defining the TensorFlow graph; Running the neural network; Summary; Chapter 3: Word2vec -- Learning Word Embeddings; What is a word representation or meaning?; Classical approaches to learning word representation. WordNet -- using an external lexical knowledge base for learning word representationsTour of WordNet; Problems with WordNet; One-hot encoded representation; The TF-IDF method; Co-occurrence matrix; Word2vec -- a neural network-based approach to learning word representation; Exercise: is queen = king -- he + she?; Designing a loss function for learning word embeddings; The skip-gram algorithm; From raw text to structured data; Learning the word embeddings with a neural network; Formulating a practical loss function; Efficiently approximating the loss function. Implementing skip-gram with TensorFlowThe Continuous Bag-of-Words algorithm; Implementing CBOW in TensorFlow; Summary; Chapter 4: Advanced Word2vec; The original skip-gram algorithm; Implementing the original skip-gram algorithm; Comparing the original skip-gram with the improved skip-gram; Comparing skip-gram with CBOW; Performance comparison; Which is the winner, skip-gram or CBOW?; Extensions to the word embeddings algorithms; Using the unigram distribution for negative sampling; Implementing unigram-based negative sampling; Subsampling -- probabilistically ignoring the common words. Implementing subsampling. TensorFlow is the leading framework for deep learning algorithms critical to artificial intelligence, and natural language processing (NLP) makes much of the data used by deep learning applications accessible to them. This book brings the two together and teaches deep learning developers how to work with today's vast amount of unstructured data. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Programming & scripting languages: general. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast Electronic book. has work: Natural language processing with TensorFlow (Text) https://id.oclc.org/worldcat/entity/E39PCGPFcfyqXFQwPxgyHWGQ9C https://id.oclc.org/worldcat/ontology/hasWork Print version: Ganegedara, Thushan. Natural Language Processing with TensorFlow : Teach language to machines using Python's deep learning library. Birmingham : Packt Publishing, ©2018 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1823678 Volltext |
| spellingShingle | Ganegedara, Thushan Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Natural Language Processing; What is Natural Language Processing?; Tasks of Natural Language Processing; The traditional approach to Natural Language Processing; Understanding the traditional approach; Example -- generating football game summaries; Drawbacks of the traditional approach; The deep learning approach to Natural Language Processing; History of deep learning; The current state of deep learning and NLP; Understanding a simple deep model -- a Fully Connected Neural Network. The roadmap -- beyond this chapterIntroduction to the technical tools; Description of the tools; Installing Python and scikit-learn; Installing Jupyter Notebook; Installing TensorFlow; Summary; Chapter 2: Understanding TensorFlow; What is TensorFlow?; Getting started with TensorFlow; TensorFlow client in detail; TensorFlow architecture -- what happens when you execute the client?; Cafe Le TensorFlow -- understanding TensorFlow with an analogy; Inputs, variables, outputs, and operations; Defining inputs in TensorFlow; Feeding data with Python code; Preloading and storing data as tensors. Building an input pipelineDefining variables in TensorFlow; Defining TensorFlow outputs; Defining TensorFlow operations; Comparison operations; Mathematical operations; Scatter and gather operations; Neural network-related operations; Reusing variables with scoping; Implementing our first neural network; Preparing the data; Defining the TensorFlow graph; Running the neural network; Summary; Chapter 3: Word2vec -- Learning Word Embeddings; What is a word representation or meaning?; Classical approaches to learning word representation. WordNet -- using an external lexical knowledge base for learning word representationsTour of WordNet; Problems with WordNet; One-hot encoded representation; The TF-IDF method; Co-occurrence matrix; Word2vec -- a neural network-based approach to learning word representation; Exercise: is queen = king -- he + she?; Designing a loss function for learning word embeddings; The skip-gram algorithm; From raw text to structured data; Learning the word embeddings with a neural network; Formulating a practical loss function; Efficiently approximating the loss function. Implementing skip-gram with TensorFlowThe Continuous Bag-of-Words algorithm; Implementing CBOW in TensorFlow; Summary; Chapter 4: Advanced Word2vec; The original skip-gram algorithm; Implementing the original skip-gram algorithm; Comparing the original skip-gram with the improved skip-gram; Comparing skip-gram with CBOW; Performance comparison; Which is the winner, skip-gram or CBOW?; Extensions to the word embeddings algorithms; Using the unigram distribution for negative sampling; Implementing unigram-based negative sampling; Subsampling -- probabilistically ignoring the common words. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Programming & scripting languages: general. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / |
| title_auth | Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / |
| title_exact_search | Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / |
| title_full | Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / Thushan Ganegedara. |
| title_fullStr | Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / Thushan Ganegedara. |
| title_full_unstemmed | Natural language processing with TensorFlow : teach language to machines using Python's deep learning library / Thushan Ganegedara. |
| title_short | Natural language processing with TensorFlow : |
| title_sort | natural language processing with tensorflow teach language to machines using python s deep learning library |
| title_sub | teach language to machines using Python's deep learning library / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Programming & scripting languages: general. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Machine learning. Artificial intelligence. Python (Computer program language) Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. Programming & scripting languages: general. Neural networks & fuzzy systems. COMPUTERS General. Artificial intelligence Machine learning Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1823678 |
| work_keys_str_mv | AT ganegedarathushan naturallanguageprocessingwithtensorflowteachlanguagetomachinesusingpythonsdeeplearninglibrary |