Natural Language Processing with PyTorch

Delip Rao, Brian McMahan

Natural Language Processing with PyTorch

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商品描述

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

商品描述(中文翻譯)

自然語言處理（NLP）為人工智慧中解決有趣問題提供了無限機會，使其成為開發智能、基於深度學習的應用的最新前沿。如果您是一位開發人員或研究人員，準備深入研究這個快速發展的人工智慧領域，本實用書將向您展示如何使用PyTorch深度學習框架來實現最近發現的NLP技術。開始之前，您只需要具備機器學習背景和使用Python進行編程的經驗。

作者Delip Rao和Goku Mohandas為您提供了PyTorch和深度學習算法的扎實基礎，用於構建涉及文本的語義表示的應用。每個章節都包含多個代碼示例和插圖。

- 深入介紹NLP、深度學習和PyTorch
- 了解傳統的NLP方法，包括NLTK、SpaCy和gensim
- 探索嵌入：語言中單詞的高質量表示
- 使用循環神經網絡（RNN）從語言序列中學習表示
- 使用複雜的神經架構（如長短期記憶（LSTM）和門控循環單元）改進RNN的結果
- 探索序列到序列模型（用於翻譯），它讀取一個序列並生成另一個序列

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