PyTorch自然语言处理实战(附详细代码下载)

2 月 12 日 专知

【导读】推荐一本PyTorch进行自然语言处理实战的书:《Natural Language Processing with PyTorch》,手把手教你PyTorch基础,以及NLP上如何一步步应用,书中提供详细代码示例,感兴趣的同学下载学习。


自然语言处理(NLP)为解决人工智能问题提供了无限机会,使亚马逊Alexa和谷歌翻译等产品成为可能。如果您是NLP和深度学习的新手,那么本实用指南将向您展示如何使用PyTorch(一个基于python的深度学习库)来应用这些方法。

 

作者Delip RaoBrianMcMahon在NLP和深度学习算法方面为您提供了坚实的基础,并演示了如何使用PyTorch构建应用程序,其中包含针对您所面临的问题的丰富文本表示。每一章包括几个代码示例和插图。

  • 探索计算图和监督学习范例

  • 掌握PyTorch优化张量操作库的基础知识

  • 概述传统的NLP概念和方法

  • 了解构建神经网络所涉及的基本思想

  • 使用嵌入来表示单词,句子,文档和其他特征

  • 探索序列预测并生成序列到序列模型

  • 学习构建生产NLP系统的设计模式


【PoTorch NLP实战代码下载】

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  • 后台回复“PytorchNLP” 就可以获取《代码》的下载链接~ 


书籍地址:

https://www.amazon.com/_/dp/1491978236

代码地址:

https://github.com/joosthub/PyTorchNLPBook



Natural Language Processing with PyTorch

Build Intelligent Language Applications Using Deep Learning 
By Delip Rao and Brian McMahan

Welcome. This is a companion repository for the book Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning.

Table of Contents

  • Get Started!

  • Chapter 1: Introduction

    • PyTorch Basics

  • Chapter 2: A Quick Tour of NLP

  • Chapter 3: Foundational Components of Neural Networks

    • In-text examples

    • Diving deep into supervised training

    • Classifying sentiment of restaurant reviews using a Perceptron

  • Chapter 4: Feed-forward Networks for NLP

    • Limitations of the Perceptron

    • Introducing Multi-layer Perceptrons (MLPs)

    • Introducing Convolutional Neural Networks (CNNs)

    • Surname Classification with an MLP

    • Surname Classification with a CNN

  • Chapter 5: Embedding Words and Types

    • Using Pretrained Embeddings

    • Learning Continous Bag-of-words Embeddings (CBOW)

    • Transfer Learning using Pre-trained Embeddings

  • Chapter 6: Sequence Modeling for NLP

    • A sequence representation for Surnames

  • Chapter 7: Intermediate Sequence Modeling for NLP

    • Generating novel surnames from sequence representations

    • Uncondition generation

    • Conditioned generation

  • Chapter 8: Advanced Sequence Modeling for NLP

    • Understanding PackedSequences

    • Sequence to Sequence Learning

    • Attention

    • Neural Machine Translation

  • Chapter 9: Classics, Frontiers, Next Steps

-END-

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