A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.
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Updated
May 10, 2024 - TypeScript
A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.
Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.
Combine grid search with early stopping via cross validation
Breast Cancer Wisconsin Dataset Classifier with Scikit-learn and Streamlit
Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
Python project for Banknotes Analysis.
A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning
Backpropagation and automatic differentiation, and grid search from scratch.
🔮 Mastermind puzzle solver using Genetic Algorithm and Grid Search for optimization
Cat vs. Dog classification model using traditional ML methods, including data collection, splitting, HOG feature extraction, model training (e.g., SVM, Decision Tree), and fine-tuning via Grid Search.
This repository contains the code and data for a comprehensive survival analysis and prediction study conducted on patients with advanced heart failure. The study focused on 299 patients classified as class III/IV heart failure.
This project aims to predict customer churn using machine learning techniques. By understanding the factors that contribute to churn, businesses can take proactive measures to retain customers and maximize their customer base. The project focuses on developing a predictive model using machine learning algorithms to forecast customer churn.
Assignments for the course Applied Machine Learning at NMBU.
hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.
Yandex Practicum Data Science project
Logistic Regression and Decision Tree models to predict Customers purchasing a Loan from the bank.
Code for 1th and 2th stage of 2020 NTI ML competition.
Prediction of molecule type in Python.
Datasets to build classification and regression models optimized via random search and grid search algorithms.
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