Natural Language Processing with PyTorch
Build Intelligent Language Applications Using Deep Learning
Natural Language Processing (NLP) offers unbounded opportunities for solving interesting problems in artificial intelligence, making it the latest frontier for developing intelligent, deep learning-based applications. If you're a developer or researcher ready to dive deeper into this rapidly growing area of artificial intelligence, this practical book shows you how to use the PyTorch deep learning framework to implement recently discovered NLP techniques. To get started, all you need is a machine learning background and experience programming with Python.
Authors Delip Rao and Goku Mohandas provide you with a solid grounding in PyTorch, and deep learning algorithms, for building applications involving semantic representation of text. Each chapter includes several code examples and illustrations.
- Get extensive introductions to NLP, deep learning, and PyTorch;
- Understand traditional NLP methods, including NLTK, SpaCy, and gensim;
- Explore embeddings: high quality representations for words in a language;
- Learn representations from a language sequence, using the Recurrent Neural Network (RNN);
- Improve on RNN results with complex neural architectures, such as Long Short Term Memories (LSTM) and Gated Recurrent Units;
- Explore sequence-to-sequence models (used in translation) that read one sequence and produce another.
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