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.


Ravi Ilango is a Principal Data Scientist at StatesTitle. He is passionate in developing deployable deep learning solutions. Previously he was at Foghorn Systems as a Sr. Data Scientist and has over 10 years of experience at Apple as a data Scientist & at Applied Materials in Supply Chain Program Management. Ravi has a Graduate Certificate in Data Mining & Machine Learning from Stanford and completed a Masters Program in Aeronautics and Production Engineering from IIT Madras. He has a BS in Mechanical Engineering, Madras University.

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