Popular GitHub Repositories for Junit Jupiter

Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog

An extensive collection of data and artificial intelligence (AI) notebook templates, curated to encompass models, analytics, code snippets, and a variety of resources. ~ Team ML Nagpur

Python机器学习基础教程，学习代码全部Python化，不需要使用Jupiter，部分采用中文说明，随带了部分读书笔记和个人理解。Notebook中文化。mglearn 和 data 整合到项目中。

JavaApp1-Jun-2022

An Ai agent to provide token and swap information. Build using eliza framework, jupiter apis, discord bot.

Project 1: ?? Movie-Recommendation-System, Project 2: ??Fake News Detection System

? Storyteling for Data Science and Artificial Intelligence - PUC-SP University

??? AI avatar generator built with Stable Diffusion.

Designed to make writing integration tests with Testcontainers in Java effortless and extensible

Stable Diffusion Automatic 1111 with support for Hugging Face and Civitai Models and LoRA

Sutton and Barto's RL Book Exercises in Jupiter Notebook (Python3)

一个模型上下文协议服务器，它允许Claude AI访问Solana上的Jupiter交易所API。

Seyahat Asistan?m - Yapay zeka ile seyahat planlama uygulamas?: hava durumu, kontrol listeleri, yerel ?neriler ve daha fazlas?.

The project is about fence inspection for breach detection using drones autonomously

一个使用 Jupiter 新版 Ultra API 在 Solana 区块链上执行代币兑换的 MCP 服务器。

Training of machine learning algorithms in order to produce the best model for average rating predictions of a book.

Natural language processing is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data

This a project which predicts the stock price of Tesla for a given time period & based upon the previous 10 years of historical data. Here, numerical and sentimental analysis is performed with the help of natural language toolkit (NLKT), Textblob, sklearn etc. By observing the previous trends of the market stock price and sentiments of the news about the market, this model predicted the future variation of the stock market price of TELSA. Basically, four models were trained with the same dataset like: Random Forest, AdaBoost, LGBM & xgboost out of these model LGBM model predicted with the least mean squared error value.

Notebooks of pre trained models using the HAM10000 dataset

基于Spring Boot 3和Java 24的OAuth 2授权服务器