jupyter python

Interactive controls for Jupyter notebooks: Python Examples

19 Sep 2018   Examples on how to add interactive controls to jupyter notebooks.

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python caching

Caching Function Results in Python: Examples using Joblib and Functools

15 Sep 2018   In many cases (web development, data analysis, etc) it's useful to be able to store results that take long to re-compute. These solutions allow you to save results (either in memory or on disk) so that all function calls after the first one become constant-time lookups.

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data-science

Risk in Machine Learning Models

06 Sep 2018   Machine Learning models can make actual decisions that affect your business. However, things can go wrong, which introduces risk that must be dealt with.

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paper-summary

Paper Summary: SMOTE: Synthetic Minority Over-sampling Technique

01 Sep 2018   Summary of the 2002 article "SMOTE: Synthetic Minority Over-sampling Technique" by Chawla et al.

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scikit-learn production

Heads-up for Deploying Scikit-learn Models to Production: Quick Checklist

01 Sep 2018   A couple of tips for addressing common problems and unexpected situations when using scikit-learn models in production..

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scikit-learn

Cross-Validation Examples with Scikit-Learn

01 Sep 2018   Using cross-validation within scikit-learn.

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pandas

Using SQL-like Syntax with Pandas Dataframe: Query and Eval Examples

05 Jul 2018   Use SQL-like syntax to perform in-place queries on pandas dataframes.

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paper-summary multi-label structured-learning hierarchical-learning natural-language-processing

Paper Summary: Multi-Label Classification on Tree- and DAG-Structured Hierarchies

02 Jul 2018   Summary of the 2011 article "Multi-Label Classification on Tree- and DAG-Structured Hierarchies" by Bi and Kwok.

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paper-summary nlp

Paper Summary: The Natural Language Decathlon

30 Jun 2018   Summary of the 2018 article "The Natural Language Decathlon" by McCann et al.

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flask scikit-learn

Example Project Template: Serve a Scikit-learn Model via a Flask API

27 Jun 2018   Full (albeit simple) example on how to create a simple Flask API to serve predictions using a pre-trained scikit-learn model. Includes supporting features such as logging, error handling, input validation, etc. Full code available on Github.

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