[ICLR2025 Spotlight🔥] 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.

Kernel objects for scaling and format conversion within VapourSynth

Python 3.12 application for scaling images using various algorithms

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

[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

This repo focuses on latency-aware resource optimization for Kubernetes

learning python day 4

My Monte-Carlo numerical simulations of competing dynamics Ising model

Neat_FrontEnd

Badanie metod skalowania w algorytmach genetycznych

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

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.

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.

Predict the quality of wine using various features from two datasets: red and white wine. It addresses the class imbalance problem prevalent in the dataset, particularly in the quality labels.

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

EDA-REGRESSION-CLASSIFICATION-WITH-BALCK-FRIDAY-DATASET

Agriculture is highly sensitive to soil and environmental variability, making crop selection a non-trivial classification problem. By applying supervised machine learning techniques to predict the most suitable crop for given farm conditions, the model enables data-driven agricultural decision-making.

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.