Fundamentals of Deep Learning for Computer Vision

Created by: Max Katz, Eric Levin

Produced in 2018

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Course Description

In this hands-on course, you will learn the basics of deep learning by training and deploying neural networks. You will:Implement common deep learning workflows such as Image Classification and Object Detection.Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.Deploy your networks to start solving real-world problems.On completion of this course, you will be able to start solving your own problems with deep learning.

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Instructor Details

Max Katz, Eric Levin

Max Katz is a solutions architect at NVIDIA, where he supports the US Department of Energy in deploying GPU-accelerated supercomputers and provides assistance to the scientific user community on the use of these systems. His background is in computational astrophysics, a field he still actively does research in. Max holds a PhD in physics from Stony Brook University and a BS and MS in physics from Rensselaer Polytechnic Institute.

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