code for studying OpenAI's CLIP explainability
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Updated
Jan 7, 2022 - Jupyter Notebook
code for studying OpenAI's CLIP explainability
This project provides a performance evaluation of credit card default prediction. Thus different models are used to test the variable in predicting the credit default and we found Random Forest Classifier performs the best with a recall of 0.95 on the test set.
Machine Learning Individual Project - November 23, 2021
Machine Learning Final Project - December 04, 2021
Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
Explaining Trees (LightGBM) with FastTreeShap (Shapley) and What if tool
This project develops an ML binary classification model to predict phishing webpages.
Example projects for Arthur Model Monitoring Platform
CrysXPP: An Explainable Property Predictor for Crystalline Materials (NPJ Computational Materials - 2022)
Capture fundamentals around ethics of AI, responsible AI from principle, process, standards, guidelines, ecosystem, regulation/risk standpoint.
This project implements an ML regression model for predicting cancer death rate in US.
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
Study on the performance of pre-trained models (VGG16, EfficientNetb0, ResNet50, ViT16) with weight fine tuning, as well as classical ML algorithms (Naive Bayes, Logistic Regression, Random Forest) on a dataset of 6.806 fungi microscopy Images utilizing Pytorch.
Writeup on classification model for predicting outcomes of NFL games, focusing on explainability. (+ project writeup)
Developed an efficient system to empower retailers with profitable insights & maintain a competitive edge in the dynamic retail industry.
A proof-of-concept for the implementation of an early fault detection system in oil wells, designed to enhance operational efficiency and reduce costs.
Java client to interact with Arize API
An application of the WhizML codebase for an analysis of cardiovascular disease risk.
A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.
Predict which powerlifters will have the highest one-rep-max deadlift
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