About

Amit Bohra is a Deep Learning Engineer with a passion for solving real-world challenges using logical, analytical, and creative abilities. With almost three years of relevant experience, he has a core area of interest in Computer Vision and Natural Language Processing. Currently working as a Computer Vision Research & Development Engineer at quantiphi, Amit has previously worked at athenasowl and techienest in similar roles. Amit has a Bachelor of Technology degree in Computer Science from jk lakshmipat university, where he graduated in 2020. Prior to this, he completed his High School Diploma in Science and Mathematics from step by step high school, jaipur in 2015. Amit is skilled in Reinforcement Learning, Computer Vision, Natural Language Processing, and Time Series Analysis. He has worked on several projects, including creating an Artificial Intelligence Program for solving Large Maze Systems, working on DeepFakes, and developing 3D Convolutional Networks for Pedestrian Action Measurement for Self Driving Car. At athenasowl, Amit worked on Scene Boundary Detection, Siamese Embeddings and Siamese Network Architecture, Transfer Learning using classical Neural Networks for Visual Embeddings, and Video and Audio Handling and Transformation using FFMPEG tool. He also implemented Focal Loss for Imbalance Dataset, got hands-on with Neural Networks using Pytorch as well as Tensorflow, and gained a basic overview of Quantum Computing. Additionally, he implemented sequential clustering having timed distance penalization. At techienest, Amit worked on various projects, including building an Artificial Intelligence Application for creating Large MAZE Systems and finding a path from any one point to another, implementing various Object Detection Techniques, and building a Smart Voice Assistant like "JARVIS" for automating various tasks using Speech Recognition. He also analyzed Churn Rate using Artificial Neural Network, performed Grid Search Techniques for better choice of Hyper Parameters Tuning, formulated K-fold Cross-Validation Technique for understanding the Bias Variance Tradeoff, and gained exposure in Web Scraping using the Beautiful Soup, Scrapy libraries in Python3. Amit's tech stack includes Computer Vision, Research Scientist, NLP, Deep Learning, Neural Networks, Reinforcement Learning, ML, Tensorflow, GCP, Big Data, Pytorch, CNN, Object detection, Keras, Scikit, Search, and Web.