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Name: BabuKaushik10
Type: User
Company: IIT Madras
Bio: MS + PhD Student
Twitter: BabuKaushik
Location: Chennai
Name: BabuKaushik10
Type: User
Company: IIT Madras
Bio: MS + PhD Student
Twitter: BabuKaushik
Location: Chennai
Analytics Club Sessions 2022
Python Accumulated Local Effects package
Algorithms for explaining machine learning models
Code for "High-Precision Model-Agnostic Explanations" paper
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)
A curated list of gradient boosting research papers with implementations.
A curated list of awesome machine learning interpretability resources.
Conditional GAN for generating synthetic tabular data.
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.
Generate Diverse Counterfactual Explanations for any machine learning model.
Dress styles generation using GANs using TensorFlow
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
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 in slanted land
Generating Tabular Synthetic Data using State of the Art GAN architecture
Prediction of spectral accelerations of aftershock ground motion with deep learning method
Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt
iml: interpretable machine learning R package
Fit interpretable models. Explain blackbox machine learning.
Interpretable Machine Learning with Python, published by Packt
Oversampling for imbalanced learning based on k-means and SMOTE
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: Explaining the predictions of any machine learning classifier
Local Interpretable Model-Agnostic Explanations (R port of original Python package)
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.