Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

Overview

Data Science 45-min Intros

Every week*, our data science team @Gnip (aka @TwitterBoulder) gets together for about 50 minutes to learn something.

While these started as opportunities to collectively "raise the tide" on common stumbling blocks in data munging and analysis tasks, they have since grown to machine learning, statistics, and general programming topics. Anything that will help us do our jobs better is fair game.

For each session, someone puts together the lesson/walk-through and leads the discussion. Presentation platforms commonly include well-written READMEs, IPython notebooks, knitr documents, interactive code sessions... the more hands-on, the better.

Feel free to use these for your own (or your team's) growth, and do submit pull requests if you have something to add.

*ok, while we try to do it every week, sometimes it doesn't happen. In that case, we try to guilt trip the person who slacked.

Current topics

Python

Bash + command-line tools

Statistics

Machine Learning

Natural Langugage Processing

Network structure

Algorithms

Engineering

Geographic Information Systems

Web development

Visualization

Databases

Owner
Scott Hendrickson
Director, Data Science
Scott Hendrickson
Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》

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Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch

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SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

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Code for the paper "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021)

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An example of semantic segmentation using tensorflow in eager execution.

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Framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample resolution

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