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Chapter 1, Lesson 1: Introduction to spaCy
Chapter 1, Lesson 5: Statistical models
Chapter 1, Lesson 10: Rule-based matching
Chapter 2, Lesson 1: Data structures (1)
Chapter 2, Lesson 4: Data structures (2)
Chapter 2, Lesson 8: Word vectors and semantic similarity
Chapter 2, Lesson 11: Combining models and rules
Chapter 3, Lesson 1: Processing pipelines
Chapter 3, Lesson 4: Custom pipeline components
Chapter 3, Lesson 8: Extension attributes
Chapter 3, Lesson 13: Scaling and performance
Chapter 4, Lesson 1: Training and updating models
Chapter 4, Lesson 5: The training loop
Chapter 4, Lesson 9: Training best practices
Chapter 4, Lesson 12: Wrapping up
Intro
What are the benefits of anchoring?
Forming a dependency arc
Where does this go and what do we hope to deliver from this?
Does coreference resolution belong in the library?
What is the problem with using human labour for data science?
Prebuilt recipes for different tasks
AI development in companies should be done in-house
Does doing the same example multiple times slow down the annotation process?
Closing Remarks
Intro to spaCy v3
Overview of the relation extraction challenge
Building the ML model: schematic overview
Implementation of the ML model in Thinc
Defining the configuration file
Overview of the TrainablePipe API
Using a custom extension attribute
Implementation of the custom pipeline component
Recap and overview
Executing the code as a spaCy project
Using a Transformer model
Summary and conclusion