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

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.

Speech to text using the DeepSpeech architecture -- Overview of the DeepSpeech model -- Speech recordings dataset -- Preprocessing the audio data -- Creating the model -- TensorBoard visualization -- State-of-the-art in speech recognition -- Summary -- Chapter 12: Text-to-Speech Using Tacotron -- Overview of text to speech -- Naturalness versus intelligibility -- How is the performance of a TTS system evaluated? -- Traditional techniques - concatenative and parametric models -- A few reminders on spectrograms and the mel scale -- TTS in deep learning -- WaveNet, in brief -- Tacotron -- The encoder -- The attention-based decoder -- The Griffin-Lim-based postprocessing module -- Details of the architecture -- Limitations -- Implementation of Tacotron with Keras -- The dataset -- Data preparation -- Preparation of text data -- Preparation of audio data -- Implementation of the architecture -- Pre-net -- Encoder and postprocessing CBHG -- Attention RNN -- Decoder RNN -- The attention mechanism -- Full architecture, with attention -- Training and testing -- Summary -- Chapter 13: Deploying Trained Models -- Increasing performance -- Quantizing the weights -- MobileNets -- TensorFlow Serving -- Exporting the trained model -- Serving the exported model -- Deploying in the cloud -- Amazon Web Services -- Google Cloud Platform -- Deploying on mobile devices -- iPhone -- Android -- Summary -- Other Books You May Enjoy -- Index.

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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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.