Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
July 2018
|
| Links: | https://ebookcentral.proquest.com/lib/unibwm/detail.action?docID=5456142 https://ebookcentral.proquest.com/lib/unibwm/detail.action?docID=5456142 |
| Abstract: | 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-voice -- Converting 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 functions -- Sigmoid -- 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 Models -- Word 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 part -- Identifying 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 -- Walking through the code -- Input -- Word embedding -- The effects of different pretrained word embeddings -- Neural network architecture -- Decoding predictions -- The training step -- Scope for improvement -- Summary -- Chapter 8: Text Generation and Summarization Using GRUs -- Generating text using RNNs -- Generating Linux kernel code with a GRU -- Text summarization -- Extractive summarization -- Summarization using gensim -- Abstractive summarization -- Encoder-decoder architecture -- Encoder -- Decoder -- News summarization using GRU -- Data preparation Encoder network -- Decoder network -- Sequence to sequence -- Building the graph -- Training -- Inference -- TensorBoard visualization -- State-of-the-art abstractive text summarization -- Summary -- Chapter 9: Question-Answering and Chatbots Using Memory Networks -- The Question-Answering task -- Question-Answering datasets -- Memory networks for Question-Answering -- Memory network pipeline overview -- Writing a memory network in TensorFlow -- Class constructor -- Input module -- Question module -- Memory module -- Output module -- Putting it together -- Extending memory networks for dialog modeling -- Dialog datasets -- The bAbI dialog dataset -- Raw data format -- Writing a chatbot in TensorFlow -- Loading dialog datasets in the QA format -- Vectorizing the data -- Wrapping the memory network model in a chatbot class -- Class constructor -- Building a vocabulary for word embedding lookup -- Training the chatbot model -- Evaluating the chatbot on the testing set -- Interacting with the chatbot -- Putting it all together -- Example of an interactive conversation -- Literature on and related to memory networks -- Summary -- Chapter 10: Machine Translation Using the Attention-Based Model -- Overview of machine translation -- Statistical machine translation -- English to French using NLTK SMT models -- Neural machine translation -- Encoder-decoder network -- Encoder-decoder with attention -- NMT for French to English using attention -- Data preparation -- Encoder network -- Decoder network -- Sequence-to-sequence model -- Building the graph -- Training -- Inference -- TensorBoard visualization -- Summary -- Chapter 11: Speech Recognition Using DeepSpeech -- Overview of speech recognition -- Building an RNN model for speech recognition -- Audio signal representation -- LSTM model for spoken digit recognition -- TensorBoard visualization |
| Umfang: | 1 Online-Ressource (vi, 291 Seiten) Illustrationen |
| ISBN: | 9781789135916 |
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| 245 | 1 | 0 | |a Hands-on natural language processing with Python |b a practical guide to applying deep learning architectures to your NLP applications |c Rajesh Arumugam, Rajalingappaa Shanmugamani |
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| 520 | 3 | |a 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-voice -- Converting 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 functions -- Sigmoid -- 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 | |
| 520 | 3 | |a TensorBoard -- The Keras library -- Summary -- Chapter 4: Semantic Embedding Using Shallow Models -- Word 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 part -- Identifying 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 -- Walking through the code -- Input -- Word embedding -- The effects of different pretrained word embeddings -- Neural network architecture -- Decoding predictions -- The training step -- Scope for improvement -- Summary -- Chapter 8: Text Generation and Summarization Using GRUs -- Generating text using RNNs -- Generating Linux kernel code with a GRU -- Text summarization -- Extractive summarization -- Summarization using gensim -- Abstractive summarization -- Encoder-decoder architecture -- Encoder -- Decoder -- News summarization using GRU -- Data preparation | |
| 520 | 3 | |a Encoder network -- Decoder network -- Sequence to sequence -- Building the graph -- Training -- Inference -- TensorBoard visualization -- State-of-the-art abstractive text summarization -- Summary -- Chapter 9: Question-Answering and Chatbots Using Memory Networks -- The Question-Answering task -- Question-Answering datasets -- Memory networks for Question-Answering -- Memory network pipeline overview -- Writing a memory network in TensorFlow -- Class constructor -- Input module -- Question module -- Memory module -- Output module -- Putting it together -- Extending memory networks for dialog modeling -- Dialog datasets -- The bAbI dialog dataset -- Raw data format -- Writing a chatbot in TensorFlow -- Loading dialog datasets in the QA format -- Vectorizing the data -- Wrapping the memory network model in a chatbot class -- Class constructor -- Building a vocabulary for word embedding lookup -- Training the chatbot model -- Evaluating the chatbot on the testing set -- Interacting with the chatbot -- Putting it all together -- Example of an interactive conversation -- Literature on and related to memory networks -- Summary -- Chapter 10: Machine Translation Using the Attention-Based Model -- Overview of machine translation -- Statistical machine translation -- English to French using NLTK SMT models -- Neural machine translation -- Encoder-decoder network -- Encoder-decoder with attention -- NMT for French to English using attention -- Data preparation -- Encoder network -- Decoder network -- Sequence-to-sequence model -- Building the graph -- Training -- Inference -- TensorBoard visualization -- Summary -- Chapter 11: Speech Recognition Using DeepSpeech -- Overview of speech recognition -- Building an RNN model for speech recognition -- Audio signal representation -- LSTM model for spoken digit recognition -- TensorBoard visualization | |
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| indexdate | 2024-12-20T18:26:45Z |
| institution | BVB |
| isbn | 9781789135916 |
| language | English |
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| physical | 1 Online-Ressource (vi, 291 Seiten) Illustrationen |
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| spelling | Arumugam, Rajesh Verfasser aut Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications Rajesh Arumugam, Rajalingappaa Shanmugamani Birmingham ; Mumbai Packt July 2018 1 Online-Ressource (vi, 291 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier 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-voice -- Converting 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 functions -- Sigmoid -- 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 Models -- Word 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 part -- Identifying 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 -- Walking through the code -- Input -- Word embedding -- The effects of different pretrained word embeddings -- Neural network architecture -- Decoding predictions -- The training step -- Scope for improvement -- Summary -- Chapter 8: Text Generation and Summarization Using GRUs -- Generating text using RNNs -- Generating Linux kernel code with a GRU -- Text summarization -- Extractive summarization -- Summarization using gensim -- Abstractive summarization -- Encoder-decoder architecture -- Encoder -- Decoder -- News summarization using GRU -- Data preparation Encoder network -- Decoder network -- Sequence to sequence -- Building the graph -- Training -- Inference -- TensorBoard visualization -- State-of-the-art abstractive text summarization -- Summary -- Chapter 9: Question-Answering and Chatbots Using Memory Networks -- The Question-Answering task -- Question-Answering datasets -- Memory networks for Question-Answering -- Memory network pipeline overview -- Writing a memory network in TensorFlow -- Class constructor -- Input module -- Question module -- Memory module -- Output module -- Putting it together -- Extending memory networks for dialog modeling -- Dialog datasets -- The bAbI dialog dataset -- Raw data format -- Writing a chatbot in TensorFlow -- Loading dialog datasets in the QA format -- Vectorizing the data -- Wrapping the memory network model in a chatbot class -- Class constructor -- Building a vocabulary for word embedding lookup -- Training the chatbot model -- Evaluating the chatbot on the testing set -- Interacting with the chatbot -- Putting it all together -- Example of an interactive conversation -- Literature on and related to memory networks -- Summary -- Chapter 10: Machine Translation Using the Attention-Based Model -- Overview of machine translation -- Statistical machine translation -- English to French using NLTK SMT models -- Neural machine translation -- Encoder-decoder network -- Encoder-decoder with attention -- NMT for French to English using attention -- Data preparation -- Encoder network -- Decoder network -- Sequence-to-sequence model -- Building the graph -- Training -- Inference -- TensorBoard visualization -- Summary -- Chapter 11: Speech Recognition Using DeepSpeech -- Overview of speech recognition -- Building an RNN model for speech recognition -- Audio signal representation -- LSTM model for spoken digit recognition -- TensorBoard visualization Shanmugamani, Rajalingappaa aut 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 9781789139495 https://ebookcentral.proquest.com/lib/unibwm/detail.action?docID=5456142 Aggregator URL des Erstveröffentlichers Volltext |
| spellingShingle | Arumugam, Rajesh Shanmugamani, Rajalingappaa Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications |
| title | Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications |
| title_auth | Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications |
| title_exact_search | Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications |
| title_full | Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications Rajesh Arumugam, Rajalingappaa Shanmugamani |
| title_fullStr | Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications Rajesh Arumugam, Rajalingappaa Shanmugamani |
| title_full_unstemmed | Hands-on natural language processing with Python a practical guide to applying deep learning architectures to your NLP applications Rajesh Arumugam, Rajalingappaa Shanmugamani |
| title_short | Hands-on natural language processing with Python |
| title_sort | hands on natural language processing with python a practical guide to applying deep learning architectures to your nlp applications |
| title_sub | a practical guide to applying deep learning architectures to your NLP applications |
| url | https://ebookcentral.proquest.com/lib/unibwm/detail.action?docID=5456142 |
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