aws-cloudwatch boto

Set up Heartbeat Monitoring on AWS Cloudwatch with Boto: Example

30 May 2021   How to create a heartbeat check on AWS Cloudwatch, so that you get notified if some event has NOT happened in the last minute/hour/days etc

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python pandas

Pandas Dataframe Examples: Styling Cells and Conditional Formatting

09 May 2021   Some examples on how to highlight and style cells in pandas dataframes when some criteria is met. Useful for analytics and presenting data.

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python regex

Python Regular Expressions: Lookahead and lookbehind examples

09 May 2021   Examples for how and when to use lookaheads and lookbehinds in python regular expressions. These are ways to create matches that depend on what came before or what came after the pattern you want to match.

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nlp preprocessing python

Normalize Text for Natural Language Processing Tasks: Reference and Examples

02 May 2021   A couple of common preprocessing tasks you need in order to be able to use raw text in NLP tools.

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Bash examples: redirecting output, error, input, etc

25 Apr 2021   Examples on how to redirect the ouutput of commands and scripts to files, STDOUT, STDERR, etc.

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Git-LFS: Reference and Examples

11 Apr 2021   Common tasks and examples related to using Github's LFS (Large File Storage) functionality

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project-review sequence-learning generative-models

Project Review: Generating Article Titles from Keywords

31 Mar 2021   Another project summary, this time for a client who needed an API-accessible ML system to generate text to be used as article titles, given a keyword as input.

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Working with Functions in Bash: Reference and Examples

31 Mar 2021   Simple examples and common use cases for functions in shell scripting using bash (bourne-again shell).

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Markdown cheatsheet and Reference: Using Images

31 Mar 2021   Quick cheatsheet with common fixes to use images in markdown documents, such as github READMEs.

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gradient-boosting feature-engineering

Features in Boosted Tree Algorithms: Engineering, Encoding, Interaction, etc

30 Mar 2021   How to encode and engineer features correctly for use in tree boosting algorithms? How are features allowed to interact and sort of information do data scientists need to provide via manual engineering? How?

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