Natural Language Processing with PyTorch

Name: Natural Language Processing with PyTorch | Build Intelligent Language Applications Using Deep Learning
Brand: O'Reilly
Price: 50.49 EUR
Availability: OnlineOnly

Build Intelligent Language Applications Using Deep Learning

Delip Rao Brian McMahan(Autor*in)

O'Reilly (Verlag)

Erschienen am 22. Januar 2019

256 Seiten

E-Book

PDF mit Adobe-DRM

Systemvoraussetzungen

978-1-4919-7820-7 (ISBN)

50,49 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Adobe-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

Weitere Details

Weitere Ausgaben

Inhalt

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 NLP
Corpora, 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 Training
Constructing 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 Inspection
Summary
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 CNN
The 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
Example: Learning the Continuous Bag of Words Embeddings
The Frankenstein Dataset
Vocabulary, Vectorizer, and DataLoader
The CBOWClassifier Model
The Training Routine
Model Evaluation and Prediction
Example: Transfer Learning Using Pretrained Embeddings for Document Classification
The AG News Dataset
Vocabulary, Vectorizer, and DataLoader
The NewsClassifier Model
The Training Routine
Model Evaluation and Prediction
Summary
References
Chapter 6. Sequence Modeling for Natural Language Processing
Introduction to Recurrent Neural Networks
Implementing an Elman RNN
Example: Classifying Surname Nationality Using a Character RNN
The SurnameDataset Class
The Vectorization Data Structures
The SurnameClassifier Model
The Training Routine and Results
Summary
References
Chapter 7. Intermediate Sequence Modeling for Natural Language Processing
The Problem with Vanilla RNNs (or Elman RNNs)
Gating as a Solution to a Vanilla RNN's Challenges
Example: A Character RNN for Generating Surnames
The SurnameDataset Class
The Vectorization Data Structures
From the ElmanRNN to the GRU
Model 1: The Unconditioned SurnameGenerationModel
Model 2: The Conditioned SurnameGenerationModel
The Training Routine and Results
Tips and Tricks for Training Sequence Models
References
Chapter 8. Advanced Sequence Modeling for Natural Language Processing
Sequence-to-Sequence Models, Encoder-Decoder Models, and Conditioned Generation
Capturing More from a Sequence: Bidirectional Recurrent Models
Capturing More from a Sequence: Attention
Attention in Deep Neural Networks
Evaluating Sequence Generation Models
Example: Neural Machine Translation
The Machine Translation Dataset
A Vectorization Pipeline for NMT
Encoding and Decoding in the NMT Model
The Training Routine and Results
Summary
References
Chapter 9. Classics, Frontiers, and Next Steps
What Have We Learned so Far?
Timeless Topics in NLP
Dialogue and Interactive Systems
Discourse
Information Extraction and Text Mining
Document Analysis and Retrieval
Frontiers in NLP
Design Patterns for Production NLP Systems
Where Next?
References
Index
About the Authors
Colophon

Systemvoraussetzungen

Als PDF speichern Als Link merken