Deep Learning for NLP (Natural Language Processing)培训

课程编码

dlfornlp

课程时长

28 小时 通常来说是4天,包括中间休息。

要求

  • An understanding of Python programming
  • An understanding of Python libraries in general

Audience

  • Programmers with interest in linguistics
  • Programmers who seek an understanding of NLP (Natural Language Processing) 

课程概览

NLP的深度学习使机器学习简单到复杂的语言处理。当前可能的任务包括语言翻译和照片的标题生成。 DL(深度学习)是ML(机器学习)的子集。 Python是一种流行的编程语言,包含用于NLP深度学习的库。 在这个有指导意义的现场培训中,参与者将学习使用Python库进行NLP(自然语言处理),因为他们创建了一个处理一组图片并生成字幕的应用程序。 在培训结束后,参与者将能够: 使用Python库设计和编写用于NLP的DL 创建读取大量图片并生成关键字的Python代码创建Python代码,从检测到的关键字中生成字幕 听众 对语言学感兴趣的程序员希望了解NLP(自然语言处理)的程序员 课程的格式 部分讲座,部分讨论,练习和沉重的练习

Machine Translated

课程大纲

Introduction to Deep Learning for NLP

Differentiating between the various types of  DL models

Using pre-trained vs trained models

Using word embeddings and sentiment analysis to extract meaning from text 

How Unsupervised Deep Learning works

Installing and Setting Up Python Deep Learning libraries

Using the Keras DL library on top of TensorFlow to allow Python to create captions

Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) to use as extended DL libraries for the purpose of creating captions. 

Using Keras on top of TensorFlow or Theano to quickly experiment on Deep Learning

Creating a simple Deep Learning application in TensorFlow to add captions to a collection of pictures

Troubleshooting

A word on other (specialized) DL frameworks

Deploying your DL application

Using GPUs to accelerate DL

Closing remarks

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