Official Implementation of TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters

A Java library for classical test theory, item response theory, factor analysis, and other measurement techniques. It provide tools commonly used in psychometrics and operational testing programs.

Yet another scaling library. Currently in maintenance-only mode.

Python 3.12 application for scaling images using various algorithms

Kernel objects for scaling and format conversion within VapourSynth

In this project I intend to predict customer churn on bank data.

My Monte-Carlo numerical simulations of competing dynamics Ising model

learning python day 4

Badanie metod skalowania w algorytmach genetycznych

[archived] Lesson files in 3L Scaling Methods for Social Science: Estimating Patterns and Preferences in Surveys and Behavior (2020 ESSEX SUMMER SCHOOL), taught by Royce Carroll

In this ML, I predict the price of houses and compare accuracy using multiple models such as random forest regressor, MLP regressor, linear regression, and XGBoost.

Neat_FrontEnd

An application that demonstrates the ability to change the scaling of the parent window through the child modal window. Written in C# WPF.

contains the basic structure that a model serving application should have. This implementation is based on the Ray Serve framework.

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

⚠️ Alpha version, Integer based scaling index list creator. Alpha version. ⚠️

Analysis will help Jamboree in understanding what factors are important in graduate admissions and how these factors are interrelated among themselves. It will also help predict one's chances of admission given the rest of the variables.

Recruiting and retaining drivers is seen by industry watchers as a tough battle for Ola. Churn among drivers is high and it’s very easy for drivers to stop working for the service on the fly or jump to Uber depending on the rates.

I have performed OCR for handwritten Hindi characters using dense neural network. In preprocessing I have applied scaling and PCA.

build a models that predicts whether an individual makes over $50,000 per year.