Deep Learning, Transfer Learning, and Model optimization with PyTorch and OpenVINO
Deep Learning, Transfer Learning, and Model optimization with PyTorch and OpenVINO


PyTorch is the fastest growing framework to build deep learning algorithms. In this hands-on workshop, we will cover the foundational elements of PyTorch and provide an intuitive understanding of model development from scratch. To solve real-world problems, we will cover a very critical area of AI called Transfer Learning, where you can build models on top of those created by Google and others. The workshop will include an overview of OpenVINO which includes a model optimizer to run fast inference with lower compute and memory requirement in production. Time permitting, we will build and train advanced detection models for different use cases. So if you are looking to expand your skill set in AI with the latest tools and techniques, this is a workshop you do not want to miss.

Learning Outcomes: At the end of this workshop, you will have a working knowledge of the PyTorch API to train your own deep learning models. You will be able to use OpenVINO to run model optimizer to use less compute and memory for deploying model inference in production.


Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogenous compute. Recently, as product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.

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