課程大綱： Natural Language Processing (NLP) with Python spaCy培訓
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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