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Hands-On Natural Language Processing with Python : A Practical Guide to Applying Deep Learning Architectures to Your NLP Applications.

Author: Rajesh Arumugam; Rajalingappaa Shanmugamani
Publisher: Birmingham : Packt Publishing Ltd, 2018.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book teaches you to leverage deep learning models in performing various NLP tasks along with showcasing the best practices in dealing with the NLP challenges. The book equips you with practical knowledge to implement deep learning in your linguistic applications using NLTk and Python's popular deep learning library, TensorFlow.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Arumugam, Rajesh
Hands-On Natural Language Processing with Python : A Practical Guide to Applying Deep Learning Architectures to Your NLP Applications
Birmingham : Packt Publishing Ltd,c2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Rajesh Arumugam; Rajalingappaa Shanmugamani
ISBN: 9781789135916 1789135915
OCLC Number: 1046620120
Notes: Description based upon print version of record.
Walking through the code
Description: 1 online resource (307 p.)
Contents: Cover; Title Page; Copyright and Credits; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Getting Started; Basic concepts and terminologies in NLP; Text corpus or corpora; Paragraph; Sentences; Phrases and words; N-grams; Bag-of-words; Applications of NLP; Analyzing sentiment; Recognizing named entities; Linking entities; Translating text; Natural Language Inference; Semantic Role Labeling; Relation extraction; SQL query generation, or semantic parsing; Machine Comprehension; Textual Entailment; Coreference resolution; Searching; Question answering and chatbots Converting text-to-voiceConverting voice-to-text; Speaker identification; Spoken dialog systems; Other applications; Summary; Chapter 2: Text Classification and POS Tagging Using NLTK; Installing NLTK and its modules; Text preprocessing and exploratory analysis; Tokenization; Stemming; Removing stop words; Exploratory analysis of text; POS tagging; What is POS tagging?; Applications of POS tagging; Training a POS tagger; Training a sentiment classifier for movie reviews; Training a bag-of-words classifier; Summary; Chapter 3: Deep Learning and TensorFlow; Deep learning; Perceptron Activation functionsSigmoid; Hyperbolic tangent; Rectified linear unit ; Neural network; One-hot encoding; Softmax; Cross-entropy; Training neural networks; Backpropagation; Gradient descent; Stochastic gradient descent; Regularization techniques; Dropout; Batch normalization; L1 and L2 normalization; Convolutional Neural Network; Kernel; Max pooling; Recurrent neural network; Long-Short Term Memory; TensorFlow; General Purpose --
Graphics Processing Unit; CUDA; cuDNN; Installation; Hello world!; Adding two numbers; TensorBoard; The Keras library; Summary Chapter 4: Semantic Embedding Using Shallow ModelsWord vectors; The classical approach; Word2vec; The CBOW model; The skip-gram model; A comparison of skip-gram and CBOW model architectures; Building a skip-gram model; Visualization of word embeddings; From word to document embeddings; Sentence2vec; Doc2vec; Visualization of document embeddings; Summary; Chapter 5: Text Classification Using LSTM; Data for text classification; Topic modeling ; Topic modeling versus text classification; Deep learning meta architecture for text classification; Embedding layer; Deep representation Fully connected partIdentifying spam in YouTube video comments using RNNs; Classifying news articles by topic using a CNN; Transfer learning using GloVe embeddings; Multi-label classification; Binary relevance; Deep learning for multi-label classification; Attention networks for document classification; Summary; Chapter 6: Searching and DeDuplicating Using CNNs; Data; Data description; Training the model; Encoding the text; Modeling with CNN; Training; Inference; Summary; Chapter 7: Named Entity Recognition Using Character LSTM; NER with deep learning; Data; Model; Word embeddings

Abstract:

This book teaches you to leverage deep learning models in performing various NLP tasks along with showcasing the best practices in dealing with the NLP challenges. The book equips you with practical  Read more...

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