Natural Language Processing (NLP) with Python spaCy培训

课程编码

spacy

课程时长

14 小时 通常来说是2天,包括中间休息。

要求

  • Python programming experience.
  • A basic understanding of statistics
  • Experience with the command line

Audience

  • Developers
  • Data scientists

课程概览

这种由讲师指导的现场培训(现场或远程)是针对希望使用spaCy处理大量文本以查找模式和获得见解的开发人员和数据科学家。

在培训结束时,参与者将能够:

  • 安装并配置spaCy。
  • 了解spaCy的Natural Language Processing (NLP)
  • 从大规模数据源中提取模式并获取业务洞察。
  • 将spaCy库与现有Web和遗留应用程序集成。
  • 将spaCy部署到实时生产环境以预测人类行为。
  • 使用spaCy预处理Deep Learning文本

课程格式

  • 互动讲座和讨论。
  • 大量的练习和练习。
  • 在实时实验室环境中亲自实施。

课程自定义选项

  • 要申请本课程的定制培训,请联系我们安排。
  • 要了解有关spaCy的更多信息,请访问:https://spacy.io/

Machine Translated

课程大纲

Introduction

  • Defining "Industrial-Strength Natural Language Processing"

Installing spaCy

spaCy Components

  • Part-of-speech tagger
  • Named entity recognizer
  • Dependency parser

Overview of spaCy Features and Syntax

Understanding spaCy Modeling

  • Statistical modeling and prediction

Using the SpaCy Command Line Interface (CLI)

  • Basic commands

Creating a Simple Application to Predict Behavior 

Training a New Statistical Model

  • Data (for training)
  • Labels (tags, named entities, etc.)

Loading the Model

  • Shuffling and looping 

Saving the Model

Providing Feedback to the Model

  • Error gradient

Updating the Model

  • Updating the entity recognizer
  • Extracting tokens with rule-based matcher

Developing a Generalized Theory for Expected Outcomes

Case Study

  • Distinguishing Product Names from Company Names

Refining the Training Data

  • Selecting representative data
  • Setting the dropout rate

Other Training Styles

  • Passing raw texts
  • Passing dictionaries of annotations

Using spaCy to Pre-process Text for Deep Learning

Integrating spaCy with Legacy Applications

Testing and Debugging the spaCy Model

  • The importance of iteration

Deploying the Model to Production

Monitoring and Adjusting the Model

Troubleshooting

Summary and Conclusion

客户评论

★★★★★
★★★★★

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