atinesh-s

atinesh-s's Stars

• Significant-Gravitas/AutoGPT

  AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

  Language:Python170k1.5k3k44.7k

• ageitgey/face_recognition

  The world's simplest facial recognition api for Python and the command line

  Language:Python53.8k1.6k1.3k13.5k

• ultralytics/yolov5

  YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

  Language:Python51.7k3729.3k16.5k

• pjreddie/darknet

  Convolutional Neural Networks

  Language:C25.9k9122.4k21.3k

• locustio/locust

  Write scalable load tests in plain Python 🚗💨

  Language:Python25.3k4241.7k3k

• spmallick/learnopencv

  Learn OpenCV : C++ and Python Examples

  Language:Jupyter Notebook21.5k88732211.6k

• deepset-ai/haystack

  AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

  Language:Python18.3k1443.6k2k

• chiphuyen/machine-learning-systems-design

  A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

  Language:HTML9.2k302101.4k

• Azure-Samples/azure-search-openai-demo

  A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

  Language:Python6.5k2301.2k4.4k

• online-ml/river

  🌊 Online machine learning in Python

  Language:Python5.1k85610555

• pytorch/serve

  Serve, optimize and scale PyTorch models in production

  Language:Java4.2k571.6k867

• maziarraissi/Applied-Deep-Learning

  Applied Deep Learning Course

  3.2k978680

• MicrosoftDocs/Virtualization-Documentation

  Place to store our documentation, code samples, etc for public consumption.

  Language:PowerShell1.8k19701.5k

• facebookresearch/DPR

  Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

  Language:Python1.7k23210306

• nlptown/nlp-notebooks

  A collection of notebooks for Natural Language Processing from NLP Town

  Language:Jupyter Notebook9723211376

• shaoanlu/fewshot-face-translation-GAN

  Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.

  Language:Jupyter Notebook7932431130

• wuhuikai/FaceSwap

  Swap face between two photos.

  Language:Python7271938226

• Ryota-Kawamura/LangChain-for-LLM-Application-Development

  In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework.

  Language:Jupyter Notebook18443149

• ksm26/LangChain-Chat-with-Your-Data

  Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.

  Language:Jupyter Notebook16332104

• justmeandopensource/elk

  Supporting Repo for ELK installation Youtube video

  Language:Shell136127372

• louisoutin/yolov5_torchserve

  Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready and real time inference.

  Language:Python9561418

• joel-simon/evo_floorplans

  Evolutionary Floorplans

  Language:Python91449

• parisnakitakejser/video-tutorial-docker

  Learn with me and get more knowledge about Docker and Docker Swarm environments.

  Language:Dockerfile545296

• black-shadows/Attendance-Using-Face-Recognition

  Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a PDF report of the attendance of the student.

  442012

• mikeffendii/Building-Systems-with-the-ChatGPT-API

  Building Systems with the ChatGPT API

  Language:Jupyter Notebook351041

• rehassachdeva/Anomaly-Detection-for-Keystroke-Dynamics

  Comparing Anomaly-Detection Algorithms for Keystroke Dynamics based on http://www.cs.cmu.edu/~keystroke/

  Language:Jupyter Notebook23315

• imshreyshah/Attendance-System-Face-Recognition

  A facial recognition based automated attendance system built using OpenCV and Django.

  Language:Python201212

• nikhilagr/User-Authentication-using-keystroke-dynamics

  Using unique typing patterns (Keystroke Dynamics) of users as signature to authenticate user and classify user as genuine or imposter. For Methodology visit: https://www.nikhildagrawal.com/keystroke.html

  Language:Jupyter Notebook19107

• YuvalNirkin/face_detection_dsfd

  Language:Python18508

• atinesh-s/sigver

  Signature verification package, for learning representations from signature data, training user-dependent classifiers.

  Language:Jupyter Notebook3000