Rishabh Misra (rishabhmisra)

rishabhmisra

Geek Repo

Company:Twitter

Location:San Francisco, CA

Home Page:rishabhmisra.github.io

Twitter:@rishabh_misra_

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Rishabh Misra's starred repositories

Book-Buddy-Interactive-Storytelling-using-a-Gemma-based-RAG

Book Buddy: Interactive Storytelling using a Gemma-based RAG 📖

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Hyper-Personalized-Ad-Campaigns-using-Generative-AI-Quick-Demo-using-Gemini-Pro-Vision

Hyper-Personalized Ad Campaigns using Generative AI: Quick Demo using Gemini Pro Vision 🎯

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tensor2tensor

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

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Public_Speaking

A repository of resources about public speaking, specifically in the context of software development and IT conferences.

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Imitating-Artistic-Style-on-Automatically-Curated-and-Shaded-Sketches

In this project, initially we curate line-art sketch from the given Manga (Japanese Comics) characters’ image using adaptive thresholding and then deploy Generative Adversarial Network to automatically shade the sketch, where compatible colours are learnt at pixel level from a large collection of images. Later in our experiment, we use a set of sample artistic images to transfer the style onto the shaded images previously generated to create artistic images of high perceptual quality using VGG-19 pre-trained model. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images.

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Forecasting-aggregate-reactions-to-blog-posts

This work focuses on estimating the reactions a blog-post might receive in the next chosen interval of time in the form of comments by users based on chief attributes of the blog-post by deploying state-of-the-art machine learning regression techniques 📝

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awesome-RecSys-papers

The awesome and classic papers in recommendation system!!! Good luck to every RecSys-learner!

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Deep-Learning-for-Recommendation-Systems

This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

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