About

Jayant Jha is a polymath with a keen interest in mathematics, data science, machine learning, and deep learning. With a strong background in mathematical modeling, he has an advanced understanding of statistical, algebraic, and other analytical techniques. He is highly motivated and diligent in his work, and he has learned machine learning and deep learning from massive collections of available literature and MOOCs. Jayant has a passion for creating neural networks with applications in mind for computer vision and natural language processing. He has expertise in various tools and technologies, including AWS, C/C++, Docker, Git, Julia, Kubernetes, Matlab, MongoDB, Python, PostgreSQL, R, Scala, and Shell Scripting. He is also skilled in data science and machine learning with NumPy, OpenCV, Scikit-learn, TensorFlow, Keras, PyTorch, PySpark, Pandas, XgBoost, and LightGBM. Jayant's research interests include deep learning, probabilistic reasoning and representation, soft computing, heuristic optimization techniques, computer vision, and natural language processing. He has a blog, cognitojayant.github.io, where he shares his knowledge and insights on these topics. Currently, Jayant is working as a Manager of Technology at arabeasy, where he supports the development of the Intelligence team and manages the priorities and workload of other team members. He also owns and develops product areas with tight integration of intelligent features. In his previous role as a Machine Learning Scientist at arabeasy, he developed and maintained high-quality and hassle-free translation systems with tight integration of machine translation. Jayant holds a Master of Technology degree in Production Engineering from NSIT, where he completed his studies in 2019. He has relevant experience of 5.69 years and has worked with various technologies such as deep learning, ML, research scientist, data scientist, computer vision, NLP, AWS, PyTorch, XGBoost, TensorFlow, OpenCV, Keras, neural networks, Python, Scikit, Scala, Docker, Pandas, MongoDB, Kubernetes, Spark, integration, Jupyter, Seaborn, CNN, Big Data, dotNET, DevOps, Flask, and GCP.