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The Resource Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing, Taweh Beysolow II

Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing, Taweh Beysolow II

Label
Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing
Title
Applied natural language processing with Python
Title remainder
implementing machine learning and deep learning algorithms for natural language processing
Statement of responsibility
Taweh Beysolow II
Creator
Author
Subject
Language
eng
Summary
Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn{u2019}t feel that you need be an expert to understand the content
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Beysolow, Taweh
Dewey number
006.3/5
Index
no index present
LC call number
QA76.9.N38
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
  • Natural language processing (Computer science)
  • Python (Computer program language)
  • Machine learning
Label
Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing, Taweh Beysolow II
Instantiates
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: What Is Natural Language Processing?; The History of Natural Language Processing; A Review of Machine Learning and Deep Learning; NLP, Machine Learning, and Deep Learning Packages with Python; TensorFlow; Keras; Theano; Applications of Deep Learning to NLP; Introduction to NLP Techniques and Document Classification; Topic Modeling; Word Embeddings; Language Modeling Tasks Involving RNNs; Summary; Chapter 2: Review of Deep Learning
  • Multilayer Perceptrons and Recurrent Neural NetworksToy Example 1: Modeling Stock Returns with the MLP Model; Learning Rate; Vanishing Gradients and Why ReLU Helps to Prevent Them; Loss Functions and Backpropagation; Recurrent Neural Networks and Long Short-Term Memory; Toy Example 2: Modeling Stock Returns with the RNN Model; Toy Example 3: Modeling Stock Returns with the LSTM Model; Summary; Chapter 3: Working with  Raw Text; Tokenization and Stop Words; The Bag-of-Words Model (BoW); CountVectorizer; Example Problem 1: Spam Detection; Term Frequency Inverse Document Frequency
  • Example Problem 2: Classifying Movie ReviewsSummary; Chapter 4: Topic Modeling and  Word Embeddings; Topic Model and Latent Dirichlet Allocation (LDA); Topic Modeling with LDA on Movie Review Data; Non-Negative Matrix Factorization (NMF); Word2Vec; Example Problem 4.2: Training a Word Embedding (Skip-Gram); Continuous Bag-of-Words (CBoW); Example Problem 4.2: Training a Word Embedding (CBoW); Global Vectors for Word Representation (GloVe); Example Problem 4.4: Using Trained Word Embeddings with LSTMs; Paragraph2Vec: Distributed Memory of Paragraph Vectors (PV-DM)
  • Example Problem 4.5: Paragraph2Vec Example with Movie Review DataSummary; Chapter 5: Text Generation, Machine Translation, and Other Recurrent Language Modeling Tasks; Text Generation with LSTMs; Bidirectional RNNs (BRNN); Creating a Name Entity Recognition Tagger; Sequence-to-Sequence Models (Seq2Seq); Question and Answer with Neural Network Models; Summary; Conclusion and Final Statements; Index
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781484237335
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Label
Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing, Taweh Beysolow II
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: What Is Natural Language Processing?; The History of Natural Language Processing; A Review of Machine Learning and Deep Learning; NLP, Machine Learning, and Deep Learning Packages with Python; TensorFlow; Keras; Theano; Applications of Deep Learning to NLP; Introduction to NLP Techniques and Document Classification; Topic Modeling; Word Embeddings; Language Modeling Tasks Involving RNNs; Summary; Chapter 2: Review of Deep Learning
  • Multilayer Perceptrons and Recurrent Neural NetworksToy Example 1: Modeling Stock Returns with the MLP Model; Learning Rate; Vanishing Gradients and Why ReLU Helps to Prevent Them; Loss Functions and Backpropagation; Recurrent Neural Networks and Long Short-Term Memory; Toy Example 2: Modeling Stock Returns with the RNN Model; Toy Example 3: Modeling Stock Returns with the LSTM Model; Summary; Chapter 3: Working with  Raw Text; Tokenization and Stop Words; The Bag-of-Words Model (BoW); CountVectorizer; Example Problem 1: Spam Detection; Term Frequency Inverse Document Frequency
  • Example Problem 2: Classifying Movie ReviewsSummary; Chapter 4: Topic Modeling and  Word Embeddings; Topic Model and Latent Dirichlet Allocation (LDA); Topic Modeling with LDA on Movie Review Data; Non-Negative Matrix Factorization (NMF); Word2Vec; Example Problem 4.2: Training a Word Embedding (Skip-Gram); Continuous Bag-of-Words (CBoW); Example Problem 4.2: Training a Word Embedding (CBoW); Global Vectors for Word Representation (GloVe); Example Problem 4.4: Using Trained Word Embeddings with LSTMs; Paragraph2Vec: Distributed Memory of Paragraph Vectors (PV-DM)
  • Example Problem 4.5: Paragraph2Vec Example with Movie Review DataSummary; Chapter 5: Text Generation, Machine Translation, and Other Recurrent Language Modeling Tasks; Text Generation with LSTMs; Bidirectional RNNs (BRNN); Creating a Name Entity Recognition Tagger; Sequence-to-Sequence Models (Seq2Seq); Question and Answer with Neural Network Models; Summary; Conclusion and Final Statements; Index
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781484237335
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote

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