Heading the data science division at Netsity, wherein I identify the machine learning models, statistical learning techniques, research hypotheses to be tested, and mapping them to the real world business problems that need to be solved. I have a solid understanding of internal workings of artificial intelligence models, underlying assumptions, mathematical algorithms, relevant datasets and model fitting, classification techniques, and model building for both supervised and unsupervised problem statements.Netsity's current focus is on computer vision, machine learning, data science, data engineering, data ops, cloud, IoT, python, .Net and deep analytics. Full Stack Development Tech Skills in Machine Learning and Computer Vision ProjectsFrameworks & ML Libraries: OpenCV, PyTorch, Keras, TensorFlow, NumPy, Scikit-learn, Pandas, Theano, SciPy, MatplotlibBig Data: Hadoop, Spark MLlib, kafkaEdge AI: Nvidia Caffe, Caffe2, nvidia CUDAModels & Approaches:Deep Learning & Neural Networks: CNN, Two-stream CNN, RNN, Mask R-CNN, ResNet, SqueezeNet, Inception ResNetLSTM, GRUPose Estimation: DensePose, OpenPoseGANSVM, Bag of Words, K-means, ORB, SIFT, Haar cascades Genetic AlgorithmsSupervised Learning, Unsupervised LearningWeb frameworks: Django, Pyramid, FlaskDatabases: PostgreSQL, MySQL, SQLite, Oracle, SQL Azure, Apache HiveNoSQL: Apache Cassandra, HBase, MongoDB, Azure CosmosDB, Amazon DynamoDBData Access: SqlAlchemy, ORM (Django)Web Services: REST, SOAP, XML-RPC, JSON-RPCI have co-founded Netsity in 1997 and helped many businesses with their data-driven decision making process. Very much hands-on with modeling and analytics and I work closely with data scientists, data engineering & digital transformation teams and our partner ecosystem.