Kata-Clean-Machine-Learning-From-Dirty-Code

Machine learning refactoring

Converting dirty machine learning code into clean, modular, and reusable components using the Pipe and Filter Design Pattern for Machine Learning.

A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.

GitHub

18 stars
3 watching
6 forks
Language: Jupyter Notebook
last commit: almost 4 years ago
Linked from 2 awesome lists


Backlinks from these awesome lists:

Related projects:

RepositoryDescriptionStars
davified/clean-code-mlAdapting clean code principles to machine learning and data science in Python714
neuraxio/neuraxleA machine learning pipeline library that enables the creation of modular and reusable data processing workflows610
neuraxio/neuraxle-tensorflowProvides utility functions and abstractions for building machine learning models using TensorFlow4
ryuk17/machinelearningThis is a collection of machine learning algorithms implemented in Python 3.6.103
jvalegre/robertAutomated machine learning protocols for cheminformatics using Python39
dirty-cat/dirty_catA Python library for handling and encoding dirty categorical data in machine learning17
pxiangwu/topofilterDevelops and evaluates machine learning algorithms to mitigate the effects of noisy labels in supervised learning.30
jwasham/machine-learningA collection of Matlab/Octave implementations of common machine learning algorithms and their underlying mathematics.62
chosj95/mimo-unetDevelops a deep learning model for single image deblurring with improved performance and computational efficiency382
msamogh/nonechucksLibrary that provides dynamic data cleaning and filtering capabilities for PyTorch datasets and samplers378
aronchick/mlops-pipelineAutomates the end-to-end machine learning workflow from code commit to model deployment18
titsuki/raku-algorithm-libsvmA Raku binding for the popular machine learning library libsvm, providing an interface to support training and evaluating Support Vector Machines.8
valdanylchuk/swiftlearnerA collection of machine learning algorithms implemented in Scala for prototyping and experimentation.39
rentruewang/koilaA lightweight wrapper around PyTorch to prevent CUDA out-of-memory errors and optimize model execution1,823
cgnorthcutt/cleanlabA tool for evaluating and improving the fairness of machine learning models57