Hands-On Natural Language Processing with PyTorch 1.x
Thomas Dop更新时间：2022-08-25 16:45:32
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Contributors About the author
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Preface
Section 1: Essentials of PyTorch 1.x for NLP
Chapter 1: Fundamentals of Machine Learning and Deep Learning
Overview of machine learning
Neural networks
NLP for machine learning
Summary
Chapter 2: Getting Started with PyTorch 1.x for NLP
Technical requirements
Installing and using PyTorch 1.x
Enabling PyTorch acceleration using CUDA
Comparing PyTorch to other deep learning frameworks
Building a simple neural network in PyTorch
NLP for PyTorch
Summary
Section 2: Fundamentals of Natural Language Processing
In this section……
Chapter 3: NLP and Text Embeddings
Technical requirements
Embeddings for NLP
Exploring CBOW
Exploring n-grams
Tokenization
Tagging and chunking for parts of speech
TF-IDF
Summary
Chapter 4: Text Preprocessing Stemming and Lemmatization
Technical requirements
Text preprocessing
Stemming and lemmatization
Uses of stemming and lemmatization
Summary
Section 3: Real-World NLP Applications Using PyTorch 1.x
Chapter 5: Recurrent Neural Networks and Sentiment Analysis
Technical requirements
Building RNNs
Introducing LSTMs
Building a sentiment analyzer using LSTMs
Deploying the application on Heroku
Summary
Chapter 6: Convolutional Neural Networks for Text Classification
Technical requirements
Exploring CNNs
Building a CNN for text classification
Summary
Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks
Technical requirements
Theory of sequence-to-sequence models
Building a sequence-to-sequence model for text translation
Next steps
Summary
Chapter 8: Building a Chatbot Using Attention-Based Neural Networks
Technical requirements
The theory of attention within neural networks
Building a chatbot using sequence-to-sequence neural networks with attention
Summary
Chapter 9: The Road Ahead
Exploring state-of-the-art NLP machine learning
Future NLP tasks
Summary
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更新时间：2022-08-25 16:45:32