About Me
Download ResumeHello, My name is Rishitosh. I am an independent, self-motivated thorough student. Being a fast learner, I love to pick up new technologies and tools. I aim to be a perfectionist and this is the reason of my never give up attitude. I am a team man who believes in the excellence of the entire team rather than an individual. I am an open-source enthusiast and have been learning from this platform since my foundation years.
- Birthday: February 14, 1998
- Location: New Delhi, India
- Email: rishitoshs@gmail.com
Skills
Android Application Development
75%Artificial Neural Networks
75%Deep Learning
35%Machine Learning
25%Python
50%Kotlin
75%Flutter
25%Big Data
50%Java
50%C / C++
75%Education
B.Tech - 8.2
August, 2016 - September, 2020Ajay Kumar Garg Engineering College
Intermediate (94.8 %)
April, 2014 - March, 2015BAL BHARATI PUBLIC SCHOOL, NEW DELHI
High School (9.6 CGPA)
April, 2012 - March, 2013BAL BHARATI PUBLIC SCHOOL, NEW DELHI
Projects
ToWatch - Movies and Playlist
ANDROID | FLASK | KOTLIN | FIREBASE REALTIME DATABASE March, 2018 – July, 2018ToWatch is a movie directory application that has multiple functionalities. It notifies the user about the latest and upcoming movies. It enables them to watch the trailer of all movies. The user can also create his personalized list of movies that he intends to watch. He can also update this list once he has watched the movies.
Saksham'18
ANDROID | KOTLIN August, 2018 – September, 2018An Android Application for College Inter-Departmental Sports Events. This project was assigned to me by the IT Department of my college. It features news, poll and medal tally of my college's annual sports Event.
TEDxAKGEC
ANDROID | KOTLIN February 2019This project was assigned to me by the IDEA lab of my college. It is a walk-through app for TEDxAKGEC event.
Publications
On the learning machine with amplificatory neuron in complex domain
Sushil Kumar, Rishitosh Kumar Singh, Aryan Chaudhary June, 2020We have proposed amplificatory neurons, using non-linear aggregation of input and weights and compared with conventional neurons using benchmark transformation, time-series and function approximation problems. Read full paper using link given below.