Hosted at github/sdpython.
Basics of Machine learning. Starting point before exploring more advanced machine learning. |
Teachings contents at ENSAE, 3 courses around algorithms, machine learning, efficient coding (with Matthieu Durut). |
Chapters from my PhD and some mathematical exploration. |
My first book on programming translated into HTML and updated from times to times. |
A few notebooks and lectures about deep learning, not more than an introduction. |
Brief introduction to Spark, first steps and some practical issues. |
Notebooks, talks about machine learning, python. |
Material about past hackathons with ENSAE students, challenges, cheat sheets. |
Various benchmarks, to promote pull request, to compare implementations, ONNX... |
This module contains the automation process used by all the modules I write including my teachings. Magic commands, Jenkins jobs, notebook conversion into slides, scripts to build setups, documentation, unit tests. |
This library explores various technics to use C or C++ functions in Python. |
Helpers for Jupyter notebooks, implements javascript additions such a menu, wraps a json viewer, a graphviz viewer. |
Implements custom windows with tkinter mostly one window which adds an entry for every parameter of a function before running it. |
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Implements functions to get insights on machine learned models or various kind of transforms to help manipulating data in a single pipeline. One example with QuantileLinearRegression which trains a linear regression with L1... |
Processes big files with pandas, too big to hold in memory, too small to be parallelized with a significant gain. The module replicates a subset of pandas API and implements other functionalities for machine learning. |
This project started with my first attempt to bring a modification to scikit-learn. My first pull request was about optimizing the computation of polynomial features. I reused the template to measure various implementations or models. |
Pieces of code to access various REST API mostly for teaching purposes. |
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Implements a light machine learning REST API based on falcon, extends it to a REST API able to publish machine learned models. |
Implements a light machine learning leaderboard based on tornado. |
Implements easy question / answers forms for teaching purposes. |
Custom RSS Reader. |
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Automates sending and grabbing mails from gmail. |
Automates modules installation mostly on Windows. |
First package created on github, created to hides some complex functions to students. |
Not really tested, implements a putty through a notebook and other helper to submit map/reduce jobs on a cluster. |
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Experiments around chatbots... Not really polished. |
A sketch to implement prediction functions for a tiny set of machine learning models in C. |
Write map/reduce in Python, translate in Python or other languages. Still a for ever progress. |
Templates every package must follow in order to be automated and publishable with pyquickhelper. |
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Wrappers for existing C libraries such as re2 |
Quotes, memories while reading books. |
Games, algorithms for kids. |
Extensions for ML.net. Fuzzy state. |
Modified version of ML.net. Even fuzzier state. |
Python + pythonnet, helpers around that combination. |
Converters for scikit-learn models and pipelines. xadupre/sklearn-onnx |
Backend for ONNX. |
ONNX, machine learning specifications used to describe machine learning prediction functions.. |
onnxruntime compiled for training |
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Training, build and run training ONNX graphs.
Python wrapper for ML.net, a C# machine learning library. |
Converters for xgboost, lightgbm, spark, xadupre/onnxmltools |
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