Name: Bharat Dadwaria
Type: User
Company: Jawaharlal Nehru Univeristy
Bio: I am a Computer Vision Research engineer exploring the field of Computer Vision and robotics for the last 3 years.
Location: New Delhi, India
Blog: https://bharatdadwaria.github.io
Bharat Dadwaria's Projects
These are the instructions for "100 Days of ML Code" By Siraj Raval on Youtube
YOLOv3 (2018) is the one of the path breaking research followed by YOLOv4 (2020) in the field of Computer Vision and Artificial Intelligence. This model can be helpful and useful in many research or industrial fields to make a change in the society.
:basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.
This repository will focus on my minor project ARP Spoofing and mitm attack report
👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla
:beers: awesome cheatsheet
:octocat: Machine Learning for Cyber Security
📚 Learn ML with clean code, simplified math and illustrative visuals. As you learn, work on interesting projects and share them on https://madewithml.com for the community to discover and learn from!
Its my github page
My implementation of useful data structures, algorithms, as well as my solutions to programming puzzles.
Best Practices, code samples, and documentation for Computer Vision.
List of Computer Science courses with video lectures.
Udacity Class CS373
UCL MSc Computational Statistics and Machine Learning Revision Notes
Collection of papers, datasets, code and other resources for object tracking and detection using deep learning
A collection of various deep learning architectures, models, and tips
This is the repository for NYU Deep learning, spring 2020, Dr Yann Le Cunn.
A toolkit for developing and comparing reinforcement learning algorithms.
A guide to contributing to open source
This is the code for "How to Simulate a Self-Driving Car" by Siraj Raval on Youtube
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Mastering Computer Vision with TensorFlow 2.0, published by Packt