Natural Language Processing (NLP) in Python tutorial given for PyCon 2020 remote conference.

Link to video: https://youtu.be/vyOgWhwUmec

Here is a list of resources helpful for items covered throughout the video

• Spacy: https://spacy.io/api
• TextBlob: https://textblob.readthedocs.io/en/dev/quickstart.html
• NLTK: https://www.nltk.org/

Overview: https://machinelearningmastery.com/gentle-introduction-bag-words-model/
Sklearn Code: https://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction

Overview: https://medium.com/@jayeshbahire/introduction-to-word-vectors-ea1d4e4b84bf
Spacy info: https://spacy.io/usage/vectors-similarity

Python overview: https://docs.python.org/3/howto/regex.html
Regex Cheatsheet: https://cheatography.com/davechild/cheat-sheets/regular-expressions/
Regex tester: https://regex101.com/
Regex golf (to practice): https://alf.nu/RegexGolf

Overview & NLTK Code: https://www.guru99.com/stemming-lemmatization-python-nltk.html
Spacy: https://spacy.io/api/lemmatizer

Quick overview + code: https://www.geeksforgeeks.org/removing-stop-words-nltk-python/

TextBlob usage: https://textblob.readthedocs.io/en/dev/api_reference.html
List of tags: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html

Attention is all you need: https://arxiv.org/pdf/1706.03762.pdf
Good overview of these architectures https://www.youtube.com/watch?v=TQQlZhbC5ps
Illustrated transfomer: http://jalammar.github.io/illustrated-transformer/

Bert: https://arxiv.org/pdf/1810.04805.pdf
OpenAI GPT: https://openai.com/blog/better-language-models/