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5-min-machine-learning

ai101

Analytics Club Sessions 2022

alepython

Python Accumulated Local Effects package

alibi

Algorithms for explaining machine learning models

anchor

Code for "High-Precision Model-Agnostic Explanations" paper

artificial-neural-network-business_case_study

Business Case Study to predict customer churn rate based on Artificial Neural Network (ANN), with TensorFlow and Keras in Python. This is a customer churn analysis that contains training, testing, and evaluation of an ANN model. (Includes: Case Study Paper, Code)

awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations.

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

ctgan

Conditional GAN for generating synthetic tabular data.

data-visualization-using-python

deep-learning-keras-tf-tutorial

Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.

dice

Generate Diverse Counterfactual Explanations for any machine learning model.

dressgan

Dress styles generation using GANs using TensorFlow

fooling-lime-shap

Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

gan-for-tabular-data

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

gans

GANs in slanted land

generating-synthetic-data-using-gans

Generating Tabular Synthetic Data using State of the Art GAN architecture

generative-adversarial-network-for-an-mnist-handwritten-digits-from-scratch-in-keras

gmpe-of-aftershocks

Prediction of spectral accelerations of aftershock ground motion with deep learning method

hands-on-gradient-boosting-with-xgboost-and-scikit-learn

Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt

iml

iml: interpretable machine learning R package

interpret

Fit interpretable models. Explain blackbox machine learning.

interpretable-machine-learning-with-python

Interpretable Machine Learning with Python, published by Packt

kmeans_smote

Oversampling for imbalanced learning based on k-means and SMOTE

lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

lime

Lime: Explaining the predictions of any machine learning classifier

lime-1

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

liquefaction-gravel-eml-2023

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.

liquefaction-xgboost-shap-jas-dodagoudar

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

machine-learning-collection

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)