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PyTorch: A flexible approach for computer vision models

Mo Patel (Independent), David Mueller (Teradata)
9:00am-12:30pm Wednesday, September 5, 2018
Implementing AI, Models and Methods
Location: Continental 7/8
Secondary topics:  Computer Vision, Deep Learning tools
Average rating: **...
(2.50, 2 ratings)

Who is this presentation for?

  • Data scientists, developers, and engineers

Prerequisite knowledge

  • A working knowledge of Python
  • Familiarity with supervised machine learning concepts

Materials or downloads needed in advance

What you'll learn

  • Understand computer vision fundamentals, convolutional neural networks, image classification, object detection, and supervised deep learning model training and deployment
  • Learn how to use PyTorch to build computer vision applications

Description

Computer vision has led the artificial intelligence renaissance. In just 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 David Mueller 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 using ONNX.

Photo of Mo Patel

Mo Patel

Independent

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.

Photo of David Mueller

David Mueller

Teradata

David Mueller is director of product management at Teradata, where he manages product innovation in AI for Teradata’s Technology Innovation Office. He focuses on technology that enables enterprises to benefit from machine and deep learning at scale. David’s background is in digital customer and marketing analytics. Previously, he headed the regional data science practice based in Singapore for Think Big Analytics, Teradata’s business analytics consultancy, where he led an international team of data scientists supporting customer projects across Southeast Asia, India, Pakistan, and South Korea as experts in the application of advanced statistical and analytical methods to the solution of business problems across industries. Earlier in his career, he led the data science team at a German ad tech company.