Popular GitHub Repositories for Return

A simple NLP library allows profiling datasets with one or more text columns. When given a dataset and a column name containing text data, NLP Profiler will return either high-level insights or low-level/granular statistical information about the text in that column.

The idea is to calculate the similarity between the resume and the job description and then return the resumes with the highest similarity score.

ScrapeGPT is a RAG-based Telegram bot designed to scrape and analyze websites, then answer questions based on the scraped content. The bot utilizes Retrieval Augmented Generation and webscraping to return natural language answers to the user's queries.

Malicious actors often reuse code to deploy their malware, phishing website or CNC server. As a result, similiaries can be found on URLs path by inspecting internet traffic. Moreover, deep learning models or even regular ML model do not fit for inline deployment in terms of running performance. However, regexes ( or YARA rules ) can be deployed on a proxy and work in real time on all the traffic. LUDA can take a set of malicious and benign URLs and return a list of regexes ready to be deployed inline !

This tool will parse the inputted text into many different existing AI writing detection tools and return the results to you. This is to be able to scan text with as many tools as possible without having to do it manually.

Demo of using a GraphQL resolver to hit a lambda function, then hit a few AI services, and return the response.

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

An MCP Server for WolframAlpha's LLM API, able to return structured knowledge & solve math

Bike sharing systems are a new generation of traditional bike rentals where the whole process from membership, rental and return back has become automatic. Through these systems, users are able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousand bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. Apart from interesting real-world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. The goal here is to build an end-to-end regression task. Here the user will provide the data and the result will be given by the best performing hype

Version 3.0.0 Pytorch implementations of DQN, DDQN, DDPG, SAC, Discrete SAC. With more features :)

An attempt to predict stock market volatility, return and trading volume of tech stocks using Semantic Vectors and Google Trends, which reflects real-time popularity of search terms

轻量化自然语言数据库查询工具，支持多轮对话完善查询目标，精确输出SQL查询语句，执行查询并返回结果。A lightweight system enbales multiple rounds of chat to transfer natural language into SQL query, execute query and return results.

Randomly partitions time series segments into train, development, and test sets; Trains multiple models optimizing parameters for development set, final cross-validation in test set; Calculates model’s annualized return, improvement from buy/hold, percent profitable trades, profit factor, max drawdown

[Machine Learning] An end-to-end MLOps app which accurately predicts handicap results in the J1 League and optimises the potential return on bets.

Suno-Deno is a library designed for interacting with the Suno AI music generation service. This library provides a comprehensive set of methods to generate songs, retrieve metadata, return song links as buffer data, and more.

??? Developed a document retrieval system to return titles of scientific papers containing the answer to a given user question based on the first version of the COVID-19 Open Research Dataset (CORD-19) ???

In this project, we deploy the Bayesian Convolution Neural Networks (BCNN), proposed by Gal and Ghahramani [2015] to classify microscopic images of blood samples (lymphocyte cells). The data contains 260 microscopic images of cancerous and non-cancerous lymphocyte cells. We experiment with different network structures to obtain the model that return lowest error rate in classifying the images. We estimate the uncertainty for the predictions made by the models which in turn can assist a doctor in better decision making. The Stochastic Regularization Technique (SRT), popularly known as Dropout is utilized in the BCNN structure to obtain the Bayesian interpretation.

Asset analysis : downloading assets and correlated assets for analysis with neural nets

Algorithmic trader with powerful & reliable strategies

This project demonstrates how to build an intelligent customer support agent using LlamaIndex, Groq’s LLaMA 3.3 70B, and HuggingFace Embeddings. The agent can answer questions about orders, return policies, delivery dates, and customer support information by combining function-calling tools and a vector-based RAG setup.