About me

Over the years I have accrued progressive experience in applied machine learning and building Deep Learning solutions to meet real-world needs, with a focus on utilization of leading architectures (DNN’s, CNN’s, Auto-encoders, Variational auto-encoders). I possess the capability to design and build an entire deep/machine learning infrastructure along with designing, 'train advanced' machine and deep learning algorithms on a GPU predominantly using TensorFlow . As a leader , I have led several big data research and data science teams while pairing with machine learning researchers, engineers to maximize the speed of experimentation and production. My interest includes: Machine Learning, Image Processing, Computer Vision, GPU Deep Learning, Algorithmic Game Theory, Reinforcement Learning, Bayesian Machine Learning, Kaggle, Autoencoders and Convolutional Neural Networks (CNN). I am skilled in Artificial Intelligence, Machine Learning, Deep Learning, Graph Analysis and Natural Language Processing (Word2Vec, Doc2Vec, Word Embeddings, Topic Modelling, Web crawling, Sentiment analysis, Facebook and Twitter analysis, Document Classification and Text Summarization). I work with Big Data analysis (unstructured data, pattern recognition, sentiment analysis, geo-spatial location and social network analysis) and A.I. applied to business and strategy and IoT. I have worked on following: ■ Machine Learning pipelines/tools: TensorFlow, Keras, Spark, R, PySpark, XGBoost, sk-learn, Python, Numpy, Scipy, OpenCL, OpenCV, Caffe, matplotlib, Pandas, seaborn, Bokeh. ■ Programming: Python (4 years), Jupyter, ■ GPU Libraries: PyCUDA, PyTorch.