Advanced NLP with spaCy · A free online course
INTERACTIVE COURSE: https://course.spacy.io/en/
spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
THIS VIDEO
00:16 – Chapter 1, Lesson 1: Introduction to spaCy
03:12 – Chapter 1, Lesson 5: Statistical models
07:11 – Chapter 1, Lesson 10: Rule-based matching
11:06 – Chapter 2, Lesson 1: Data structures (1)
13:47 – Chapter 2, Lesson 4: Data structures (2)
15:58 – Chapter 2, Lesson 8: Word vectors and semantic similarity
19:58 – Chapter 2, Lesson 11: Combining models and rules
23:36 – Chapter 3, Lesson 1: Processing pipelines
26:23 – Chapter 3, Lesson 4: Custom pipeline components
29:16 – Chapter 3, Lesson 8: Extension attributes
32:33 – Chapter 3, Lesson 13: Scaling and performance
35:02 – Chapter 4, Lesson 1: Training and updating models
39:00 – Chapter 4, Lesson 5: The training loop
42:36 – Chapter 4, Lesson 9: Training best practices
45:01 – Chapter 4, Lesson 12: Wrapping up
SPACY
● Website & documentation: https://spacy.io
● GitHub: https://github.com/explosion/spaCy
● Online course: https://course.spacy.io
● Prodigy annotation tool: https://prodi.gy
FOLLOW US
● Ines Montani: https://twitter.com/_inesmontani
● spaCy: https://twitter.com/spacy_io
● Explosion: https://twitter.com/explosion_ai
Видео Advanced NLP with spaCy · A free online course канала Explosion
spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
THIS VIDEO
00:16 – Chapter 1, Lesson 1: Introduction to spaCy
03:12 – Chapter 1, Lesson 5: Statistical models
07:11 – Chapter 1, Lesson 10: Rule-based matching
11:06 – Chapter 2, Lesson 1: Data structures (1)
13:47 – Chapter 2, Lesson 4: Data structures (2)
15:58 – Chapter 2, Lesson 8: Word vectors and semantic similarity
19:58 – Chapter 2, Lesson 11: Combining models and rules
23:36 – Chapter 3, Lesson 1: Processing pipelines
26:23 – Chapter 3, Lesson 4: Custom pipeline components
29:16 – Chapter 3, Lesson 8: Extension attributes
32:33 – Chapter 3, Lesson 13: Scaling and performance
35:02 – Chapter 4, Lesson 1: Training and updating models
39:00 – Chapter 4, Lesson 5: The training loop
42:36 – Chapter 4, Lesson 9: Training best practices
45:01 – Chapter 4, Lesson 12: Wrapping up
SPACY
● Website & documentation: https://spacy.io
● GitHub: https://github.com/explosion/spaCy
● Online course: https://course.spacy.io
● Prodigy annotation tool: https://prodi.gy
FOLLOW US
● Ines Montani: https://twitter.com/_inesmontani
● spaCy: https://twitter.com/spacy_io
● Explosion: https://twitter.com/explosion_ai
Видео Advanced NLP with spaCy · A free online course канала Explosion
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