Popular GitHub Repositories for Hope Rf

This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.

RF-DETR is a real-time object detection model architecture developed by Roboflow, released under the Apache 2.0 license.

Tool for onnx->keras or onnx->tflite. Hope this tool can help you.

Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of separating sources from mixtures.

A software tool that generates simulated radar signals and creates RF datasets for developing and testing machine/deep learning detection algorithms.

Lora/LoraWan Arduino board

RF Genesis: Zero-Shot Generalization of mmWave Sensing through Simulation-Based Data Synthesis and Generative Diffusion Models (SenSys'23)

Real-time Intrusion Detection System implementing Machine Learning. We combine Supervised Learning (RF) for detecting known attacks from CICIDS 2018 & SCVIC-APT datasets, and Unsupervised Learning (AE) for anomaly detection.

RF-Next: Efficient Receptive Field Search for CNN(TPAMI2022, CVPR2021) Try it, you wouldn't regret it!

Digikala online market has recently published some open source data in various categories. Since I always wanted to do some NLP project, so I thought of some useful tutorials in python for newcomers. I really hope this could be useful for you guys. I still keep updating the package and also will share the link of video and article related to this post soon!

FaceDetect is a face detection and recognition framework built in Python on top of the work of several open source projects and models with the hope to reduce the entry barrier for developers and to encourage them to focus more on developing innovative applications that make use of face detection and recognition.

Fraud detection using lgb, catboost, rf, etc

All of my Deep Learning experiments have been summarized in this repository.It includes Pytorch tutorials, SoTA Neural Network classification ,Time Series Analysis, Collaborative Filtering . Hope you find this interesting. Check out my blog where I've provided a detail walkthrough of this repository.

This project has implemented the RAG function on Jetson and supports TXT and PDF document formats. It uses MLC for 4-bit quantization of the Llama2-7b model, utilizes ChromaDB as the vector database, and connects these features with Llama_Index. I hope you like this project.

This is an implementation of an adaptive cruise control system based on a computer vision pipeline. This work is based on YOLACT, a State-Of-The-Art real-time instance segmentation network. You're welcome to test and try our code, we hope you'll enjoy this work!

用于射频目标检测 (RF-DETR) 的 ONNX 模型，包含推理和可视化脚本

S.C.A.R.L.E.T (Sorta Crappy Assistant Robot Lazily Engineered Today) To be honest making up that acronym was easier than making this virtual assistant.'She' obeys some of the following commands:search + (what you want to search for),email-opens Gmail.What time is it?What date is it?Youtube-opens Youtube,shut down,Scarlet (check for responsiveness).This virtual assistant is very far from perfect but I hope to improve it as I continue to learn the deeper workings of the Python programming language. I could use some help with her later versions..

This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

This project focuses on classifying spam/ham emails, using machine learning algorithms like LGR, NB, RF, DT etc.. and based on the accuracy score and precision score I chose logistic regression for the classification. And I have used streamlit for frontend.

My Microsoft Copilot for Security contribution to the world! I hope this helps you!