Hands-On Natural Language Processing with PyTorch 1.x: Build smart, AI-driven linguistic applications using deep learning and NLP techniques

Thomas Dop (Author)

  • 出版商: Packt Publishing
  • 出版日期: 2020-07-09
  • 售價: $1,770
  • 貴賓價: 9.5$1,682
  • 語言: 英文
  • 頁數: 278
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789802741
  • ISBN-13: 9781789802740
  • 相關分類: DeepLearningText-mining
  • 海外代購書籍(需單獨結帳)

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商品描述

In the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks.

Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots.

By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them.

作者簡介

Thomas Dop is a data scientist at MagicLab, a company that creates leading dating apps, including Bumble and Badoo. He works on a variety of areas within data science, including NLP, deep learning, computer vision, and predictive modeling. He holds an MSc in data science from the University of Amsterdam.

目錄大綱

  1. Fundamentals of Machine Learning and Deep Learning
  2. Getting Started with PyTorch 1.x for NLP
  3. NLP and Text Embeddings
  4. Text Preprocessing, Stemming, and Lemmatization
  5. Recurrent Neural Networks and Sentiment Analysis
  6. Convolutional Neural Networks for Text Classification
  7. Text Translation using Sequence to Sequence Neural Networks
  8. Building a Chatbot Using Attention-based Neural Networks
  9. The Road Ahead