resources

Workshop resources

General

Data team notes on each project can be found here (for better or for worse).

Last year's repository is also available, which contains a list of resources and tutorials (from the first week of the workshop).

Python

First, a list of documentation for major packages:

• scikit-learn for scientific computaiton and some machine learning
• pandas for dataframes
• numpy for fast matrix operations
• dask for distributed dataframes
• PyTorch for dynamically constructed neural networks in Python. Well documented.
• keras for neural networks built on Tensorflow or Theano. Visit the keras blog for myriad tutorials. Documentation is only so-so.

Also, a long list of useful Python-related tutorials.

NLP

• nltk and nltk book (there's a whole Github organization for it)
• torchtext for NLP. New and maybe a little flaky as a result.
• "Working with text data" in sklearn
• Tutorial for creating a chat bot with deep learning and tensorflow in Python
• PyTextRank
• A tutorial by Dhananjay Bhaskar (formerly UBC, now Brown) on using nltk to analyze movie ratings.

Other

• Image hashing
• keras cheatsheet

Computing

• Git documentation
• git and bash tutorial by Patrick Walls
• Accessing a GPU on Google CoLab. Another one here.

Tutorials

• Kalman filters
• Latent dirichlet allocation
• Visit the sklearn documentation for several tutorials on popular machine learning methods like Naive Bayes.