Isha Nayak

Fresh Graduate

Honest, punctual and ready to take up exciting new challenges.

admscz@r.postjobfree.com 722-***-****

• Built a website using MEAN stack.

• Worked as part of the front-end development team.

• Project provides solutions that enable clients to create and manage virtual marketplace wherein sellers are provided with the opportunity to offer their products and services on sale,for a specified time period,to the highest bidder

Education

B. Tech.

Jaypee University of Engineering and

Technology (Guna)

Computer science

2016-2020 GPA: 6.7/10

EXPERIENCE

Summer Intern’19

Ypilson It Solutions(Indore)

06/2019-07/2019(4 weeks)

Certificates and Extra Courses

• AWS-Data Analytics Fundamentals

(2021)

• The joy of computing with java(2018)

Skill-set

Position Of Responsibility

• Active member of media wing.

• Co-ordinated an event in College

cultural fest.

• Volunteered in College Technical

Fest.

• Joint Secretary of Multimedia team

of CSI.

Other Interests

• Avid puzzle solver and amateur

Badminton player.

• Hobbies include: cooking, lisening

music Swimming, Photography.

Guna India

Style Transfer

• To develop a neural style model transfer model that can transfer the style of one image to the content of another.

• Language-Python-As it provides concise and readable code and extensive selection of libraries and framework.

• Machine Learning Framework-Pytorch-it uses dynamic computation graphs and provide python debuggers.

• Dataset-COCO image dataset-COCO is a large-scale object detection,segmentation,and captioning dataset.

PROJECTS

.

Analysis of Neural Style Transfer :, Pandit Shivangi Sharma, Rashmi agnihotri, Isha Nayak

• Analysis of the working of Neural Style Transfer.

• The analysis is done on the basis of the learning rate, the number of input images, with and without image processing, confusion matrix and accuracy. Analysis of Neural Style Transfer :Pandit Shivangi Sharma, Rashmi agnihotri, Isha Nayak.

• Training a style transfer model requires two networks: a pre-trained feature extractor and a transfer network.

• Some layers learn to extract the content of an image .

• The pre-trained model enables us to compare the content and style of two images

• The transfer network is an image translation network that takes one image as input and outputs another image.

• Transfer networks typically have an encode-decoder architecture. linkedin.com/in/ishanayak

Schooling

• Intermediate by Shivpuri

Public School,Shivpuri.

• High School by Delhi Public

School,Vijaipur

Competitive Programming

Online Coding Platforms

• Solved 500+ different problems on various platforms.

• Solved monthly challenges on Leetcode.

PAPERS & PUBLICATIONS

Languages: C,C++,Java

Database: My SQL(basic)

Interest: DBMS, Data Structure,

Algorithm,OOPs.

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