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Natural Language Processing with PyTorch Build Intelligent Language Applications Using Deep Learning 1st Edition by Delip Rao, Brian McMahan 1491978236 9781491978238

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ISBN 10: 1491978236

ISBN 13: 9781491978238

Author: Delip Rao, Brian McMahan

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. 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. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems

Natural Language Processing with PyTorch Build Intelligent Language Applications Using Deep Learning 1st Table of contents:

1. Introduction

  • The Supervised Learning Paradigm
  • Observation and Target Encoding
  • One-Hot Representation
  • TF Representation
  • TF-IDF Representation
  • Target Encoding
  • Computational Graphs
  • PyTorch Basics: Installation, Tensors, Operations, CUDA Tensors
  • Exercises, Solutions, Summary, References

2. A Quick Tour of Traditional NLP

  • Corpora, Tokens, and Types
  • N-grams
  • Lemmas and Stems
  • Sentence and Document Categorization
  • POS Tagging, Chunking, Named Entity Recognition
  • Sentence Structure
  • Word Senses and Semantics
  • Summary, References

3. Foundational Components of Neural Networks

  • The Perceptron
  • Activation Functions (Sigmoid, Tanh, ReLU, Softmax)
  • Loss Functions
  • Supervised Training
  • Evaluation Metrics and Dataset Splits
  • Hyperparameter Tuning, Regularization
  • Example: Sentiment Classification of Restaurant Reviews
  • Summary, References

4. Feed-Forward Networks for NLP

  • Multilayer Perceptron (MLP)
  • Example: Surname Classification
  • Convolutional Neural Networks (CNNs)
  • CNN Hyperparameters, Pooling, BatchNorm, Residual Blocks
  • Summary, References

5. Embedding Words and Types

  • Importance and Efficiency of Embeddings
  • Learning Word Embeddings
  • Example: CBOW Embeddings
  • Transfer Learning with Pretrained Embeddings
  • Summary, References

6. Sequence Modeling for NLP

  • Introduction to RNNs
  • Implementing an Elman RNN
  • Example: Surname Nationality Classification with Character RNN
  • Summary, References

7. Intermediate Sequence Modeling for NLP

  • Challenges with Vanilla RNNs
  • Gated RNNs (GRU)
  • Example: Surname Generation
  • Training Tips and Tricks
  • References

8. Advanced Sequence Modeling for NLP

  • Sequence-to-Sequence Models, Encoder-Decoder Models
  • Bidirectional RNNs, Attention Mechanisms
  • Example: Neural Machine Translation
  • Summary, References

9. Classics, Frontiers, and Next Steps

  • Key NLP Topics
  • Dialogue and Interactive Systems
  • Information Extraction, Text Mining, Document Retrieval
  • Frontiers and Production NLP
  • Future Directions
  • References, Index

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