About

Sarabjeet Singh is a highly experienced Machine Learning professional with over 13 years of relevant experience. He has a strong background in developing and implementing innovative solutions in areas such as IOT, RFID, and Telecom. Sarabjeet has extensive experience in Deep Reinforcement Learning and Unsupervised Clustering with Neural Networks. He has developed Machine Learning models using deep CNN networks for face recognition, bridge traffic analysis, and number of people in an area from a CCTV surveillance cam. Sarabjeet holds five patents in fault-tolerant parallel computers and high-speed serializer-deserializer circuits. He has also led NoSQL, Hadoop, OpenStack, SDN, NFV, RFID Assets Management, and Time series streaming data analytics teams. He has managed vendor management and worked with strategic partners such as Samsung, Tessolve, and Bayside. Sarabjeet has handled projects with revenue of $4M and IP delivery on $50M projects. Currently, Sarabjeet is working on a Deep Reinforcement Learning project as a Deep Reinforcement Learning in Tic-Tac-Toe Game: Machine Learning. He has used two dueling networks learning from each other, which eliminates the need for a training dataset of past games. This method has several advantages, including unsupervised training, no labeled data for training, and a simple advantage function for rewards. In his past roles, Sarabjeet has worked on several Machine Learning projects, including Unsupervised Machine Learning: Patient Clustering with Neural Network and Deep Convolutional Neural Network for Face Recognition. He has also completed a Machine Learning course by Andrew Ng and has a Bachelor of Technology degree in Electrical and Electronics Engineering from the Indian Institute of Technology. Sarabjeet's technical expertise includes Reinforcement Learning, ML, CNN, Neural Networks, Search, Deep Learning, NoSQL, Hadoop, and Mobile. He is a highly skilled professional with a proven track record of delivering successful projects.