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Natural language processing with PyTorch : build intelligent language applications using deep learning

Author: Delip Rao; Brian McMahan
Publisher: Beijing : O'Reilly Media, 2019.
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:

If you're a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.

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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)982651169
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Delip Rao; Brian McMahan
ISBN: 9781491978207 1491978201
OCLC Number: 1082856371
Description: 1 online resource. : color illustrations
Contents: Cover; Copyright; Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledments; Chapter 1. Introduction; The Supervised Learning Paradigm; Observation and Target Encoding; One-Hot Representation; TF Representation; TF-IDF Representation; Target Encoding; Computational Graphs; PyTorch Basics; Installing PyTorch; Creating Tensors; Tensor Types and Size; Tensor Operations; Indexing, Slicing, and Joining; Tensors and Computational Graphs; CUDA Tensors; Exercises; Solutions; Summary; References Chapter 2. A Quick Tour of Traditional NLPCorpora, Tokens, and Types; Unigrams, Bigrams, Trigrams, ..., N-grams; Lemmas and Stems; Categorizing Sentences and Documents; Categorizing Words: POS Tagging; Categorizing Spans: Chunking and Named Entity Recognition; Structure of Sentences; Word Senses and Semantics; Summary; References; Chapter 3. Foundational Components of Neural Networks; The Perceptron: The Simplest Neural Network; Activation Functions; Sigmoid; Tanh; ReLU; Softmax; Loss Functions; Mean Squared Error Loss; Categorical Cross-Entropy Loss; Binary Cross-Entropy Loss Diving Deep into Supervised TrainingConstructing Toy Data; Putting It Together: Gradient-Based Supervised Learning; Auxiliary Training Concepts; Correctly Measuring Model Performance: Evaluation Metrics; Correctly Measuring Model Performance: Splitting the Dataset; Knowing When to Stop Training; Finding the Right Hyperparameters; Regularization; Example: Classifying Sentiment of Restaurant Reviews; The Yelp Review Dataset; Understanding PyTorch's Dataset Representation; The Vocabulary, the Vectorizer, and the DataLoader; A Perceptron Classifier; The Training Routine Evaluation, Inference, and InspectionSummary; References; Chapter 4. Feed-Forward Networks for Natural Language Processing; The Multilayer Perceptron; A Simple Example: XOR; Implementing MLPs in PyTorch; Example: Surname Classification with an MLP; The Surnames Dataset; Vocabulary, Vectorizer, and DataLoader; The SurnameClassifier Model; The Training Routine; Model Evaluation and Prediction; Regularizing MLPs: Weight Regularization and Structural Regularization (or Dropout); Convolutional Neural Networks; CNN Hyperparameters; Implementing CNNs in PyTorch Example: Classifying Surnames by Using a CNNThe SurnameDataset Class; Vocabulary, Vectorizer, and DataLoader; Reimplementing the SurnameClassifier with Convolutional Networks; The Training Routine; Model Evaluation and Prediction; Miscellaneous Topics in CNNs; Pooling; Batch Normalization (BatchNorm); Network-in-Network Connections (1x1 Convolutions); Residual Connections/Residual Block; Summary; References; Chapter 5. Embedding Words and Types; Why Learn Embeddings?; Efficiency of Embeddings; Approaches to Learning Word Embeddings; The Practical Use of Pretrained Word Embeddings.
Responsibility: Delip Rao and Brian McMahan.

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