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
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