jupyterlab jupyter-notebooks

JupyterLab: Sane Defaults and Extensions for Basic DS Work

08 Nov 2021   Minimum Viable Jupyterlab configuration for reasonably pleasant DS work in Python

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Git Examples: Searching the Git History

01 Nov 2021   Examples on how to search the git logs and history for modified files, modified code, how to retrieve past files from the history, etc.

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Bash Options with Set: Examples and reference

31 Oct 2021   Examples and reference for most commonly-used bash options you can set/unset with the set builtin.

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paper-summary natural-language-processing

Paper Summary: Exploring the Limits of Transfer Learning with a Unified Text-to-text Transformer

29 Aug 2021   Summary of the 2020 article "Exploring the Limits of Transfer Learning with a Unified Text-to-text Transformer" by Raffel et al.

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python files

Python 3 Examples: Creating, Deleting, Updating directories

08 Aug 2021   Lots of examples on how to create, remove, change and otherwise interact with directories with Python 3+ (Assuming unix-like Operating Systems such as Linux and MacOS)

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Measuring Function Running Times in Python

08 Jul 2021   Really simple code to help profile individual method calls to find bottlenecks in Python code.

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matplotlib formatting

Matplotlib examples: Number Formatting for Axis Labels

29 Jun 2021   Some examples on how to properly format axis labels, add thousands separator, format axis labels to make them easier to read, etc.

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Matplotlib Examples: Plots with String Axis Labels

28 Jun 2021   Examples on how to create plots where one of the axis is not a list of integers/floats but a list of strings.

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paper-summary machine-learning-engineering machine-learning

Paper Summary: Identifying Mislabeled Instances in Classification Datasets

28 Jun 2021   Summary of the 2019 article "Identifying Mislabeled Instances in Classification Datasets" by Mueller and Markert.

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scalability software-architecture

Probabilistic Behaviour can Sometimes Replace State at Scale

28 Jun 2021   You can sometimes forgo keeping state for an application if you are operating at scale and you can afford some innacuracies.

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