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Typing SVG

irfnrdh
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export default () => ({
  notionUrl: "https://kodekeras.my.id/",
  askMeAbout: [
    "life", "idea"
  ],
  technologies: {
    "I just learn everything on the trendline since 2008"
  },
  skills: [
    "thinker", "architect"
  ],
  interest: [
    "Technology", "Programming", "Reading", "Serial Entrepreneur"
    "Writing", "Psychology", "Philosophy", "agent of change"
  ],
  simpleThing:[
    "Part time artist, full time creator",
    "Learn. Create. Develop. Share"
  ]
});

🌱 I’m currently learning to build Ruema since covid-19 start March 2020

💬 Ask me anything about life & idea

📧 Reach me: [email protected]

👾 Check out dailynotes on Twitter

💻 Visit Bad website 🌐 / 🌐


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Irfannur Diah's Projects

deeppavlov icon deeppavlov

An open source library for deep learning end-to-end dialog systems and chatbots.

defi-developer-road-map icon defi-developer-road-map

DeFi Developer roadmap is a curated Web3.0 Developer handbook which includes a list of the best tools for DApps, development resources and lifehacks.

design-patterns icon design-patterns

A curated list of software and architecture related design patterns.

dl-4-tsc icon dl-4-tsc

Deep Learning for Time Series Classification

dm_env icon dm_env

A Python interface for reinforcement learning environments

dotfiles-1 icon dotfiles-1

:wrench: .files, including ~/.macos — sensible hacker defaults for macOS

erp-nuxt icon erp-nuxt

A ERP-Nuxt is a project Enterprise Resource Planning

evilusb icon evilusb

Quick utility to craft executables for pentesting and managing reverse shells

fake-news-detection icon fake-news-detection

Machine learning Based Minor Project, which uses various classification Algorithms to classify the news into FAKE/REAL, on the basis of their Title and Body-Content. Data has been collected from 3 different sources and uses algorithms like Random Forest, SVM, Wordtovec and Logistic Regression. It gave 94% accuracy.

fake-news-detection-pipeline icon fake-news-detection-pipeline

Pipeline for detecting fake news, covering data ingestion, doc embedding, classifier hypertuning & model ensembling. Quick walkthrough available in README. Execution logs on my FloydHub page.

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