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April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

PyTorch: A flexible approach for computer vision models

Mo Patel (Independent)
1:40pm–5:10pm Monday, April 30, 2018
Implementing AI, Models and Methods
Location: Nassau East/West
Average rating: ***..
(3.67, 3 ratings)

Who is this presentation for?

  • Data scientists and application developers

Prerequisite knowledge

  • A working knowledge of supervised machine learning concepts and Python

Materials or downloads needed in advance

What you'll learn

  • Understand the fundamentals of computer vision, convolutional neural networks, and PyTorch
  • Learn how to use PyTorch for object classification understanding and object detection and how to put PyTorch models into production via Caffe2


Computer vision has led the artificial intelligence renaissance. In a few short years, we have seen advancements in computer vision research turned into production-level systems from web applications to mobile devices and the automotive industry, to name a few. Much of this progress is derived from convolutional neural networks (CNNs), and techniques such as object classification, localization and detection, tracking, and segmentation are foundational concepts for most vision-based applications today.

PyTorch enables researchers and practitioners to more easily build CNNs for computer vision. Released in early 2017, it has increasingly gained popularity in the computer vision community. Mo Patel and Neejole Patel offer an overview of computer vision fundamentals and walk you through PyTorch code explanations for notable objection classification and object detection models. There will be an equal balance of theory and hands-on PyTorch coding. You’ll also learn how to deploy PyTorch models into production via Caffe2.

Photo of Mo Patel

Mo Patel


Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.

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04/30/2018 10:17am EDT

The presentation is here

04/30/2018 8:03am EDT

Is the presentation online somewhere?