hariharan1309

hariharan1309's Stars

• expo/expo

  An open-source framework for making universal native apps with React. Expo runs on Android, iOS, and the web.

  Language:TypeScript36.3k5.8k

• ZBSTPlus/UMS-FE

  UMS FrontEnd Application

  Language:JavaScript211

• Varun-L/ngen-basic

  Language:JavaScript11

• NibiruChain/ts-sdk

  TypeScript-based client for interacting with the Nibiru blockchain

  Language:TypeScript368

• Cyfrin/foundry-full-course-cu

  4k993

• cometchat/cometchat-uikit-react

  Voice & Video Calling and Text Chat App for React (JavaScript/Web)

  Language:TypeScript343390

• maizzle/maizzle

  Quickly build HTML emails with Tailwind CSS.

  Language:HTML2.4k62

• mailchimp/mailchimp-marketing-node

  The official Node.js client library for the Mailchimp Marketing API

  Language:JavaScript15642

• agnitas-org/openemm

  Web-based software for email automation and marketing (newsletters, multi-stage mail campaigns, transaction mails, autoresponder, etc.)

  Language:JavaScript11335

• mettle/sendportal

  Open-source self-hosted email marketing. Manage your own newsletters at a fraction of the cost.

  Language:PHP1.8k371

• threeheartsdigital/emailoctopus-templates

  Beautiful responsive HTML email marketing templates for use in marketing campaigns. Brought to you by EmailOctopus.

  Language:HTML9833

• mjmlio/mjml

  MJML: the only framework that makes responsive-email easy

  Language:JavaScript17.2k967

• freeCodeCamp/mail-for-good

  An open source email campaign management tool for nonprofits

  Language:JavaScript3.8k475

• RameshMF/RameshMF

  Check out my blogs, videos and projects.

  5217

• iamneo-production/4e0b2c8f-dfc4-450c-8a31-412bcf206b58

  https://sonarcloud.io/summary/overall?id=examly-test_4e0b2c8f-dfc4-450c-8a31-412bcf206b58

  Language:JavaScript81

• VirajShah21/WebOS

  A simulated operating system for the web. Simply put, it is a wrapper for react components.

  1

• HeyPuter/notepad

  An example project demonstrating the Puter.js SDK

  Language:JavaScript617

• twitter/the-algorithm

  Source code for Twitter's Recommendation Algorithm

  Language:Scala62.8k12.2k

• Picsart-AI-Research/Text2Video-Zero

  [ICCV 2023 Oral] Text-to-Image Diffusion Models are Zero-Shot Video Generators

  Language:Python4.1k357

• SamPink/dev-gpt

  dev-gpt, an automated python developer

  Language:Python20440

• vfsfitvnm/ViMusic

  An Android application for streaming music from YouTube Music.

  Language:Kotlin9.2k962

• webtor-io/embed-sdk-js

  Webtor.io embed SDK for online torrent streaming and download on your site

  Language:HTML36985

• godliver/source-code-BBW-BBS

  Implementation-BBW and BBS Diseases

  Language:C54

• eazybytes/spring

  Spring, SpringBoot, JPA, Hibernate : Zero To Master - Code Examples

  Language:CSS457587

• andresWeitzel/Modulo_GPT-J-6B_NLP_NodeJs

  Módulo para el Procesamiento de Lenguaje Natural de código abierto (GPT-J-6B).

  Language:JavaScript1

• AarohiSingla/ResNet50

  Language:Jupyter Notebook1713

• AarohiSingla/Faster-R-CNN-on-Custom-Dataset

  Implemented Faster R CNN on Custom Dataset

  Language:Jupyter Notebook2231

• mrdbourke/pytorch-deep-learning

  Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

  Language:Jupyter Notebook11.8k3.4k

• pyGuru123/Mini-JS

  Few projects written with vanilla javascript

  Language:JavaScript4

• mehra-deepak/Plant-Disease-Detection

  Plant Disease Detection is one of the mind-boggling issues when we talk about using Technology in Agriculture. Although researches have been done to detect whether a plant is healthy or diseased using Deep Learning and with the help of Neural Network, new techniques are still being discovered. For Fewer Data Classical Machine Learning Models are said to outstand given the data is pre-processed well. On the same theory here is my approach for Detecting whether a plant leaf is healthy or unhealthy by utilizing the classical Machine Learning Models, Pre-processing the Image Data. The data was fed to 7 Machine Learning Models with 10 fold cross-validation out of which Random Forest Classifier outperformed all the other models giving an accuracy of 97% on the test set.

  Language:Jupyter Notebook18385