Course Number: SC22

Fundamentals of Deep Learning
Instructor: Raymond Ptucha, Apple Inc.
Level: Intermediate
Duration: 4 Hours plus 30-minute break and 30-minute post-class discussion
Course Date/Time:

San Francisco: Wednesday 12 January, 07:00 - 12:00
New York: Wednesday 12 January, 10:00 - 15:00
Paris: Wednesday 12 January, 16:00 - 21:00
Tokyo: Thursday 13 January, 00:00 - 05:00

Prerequisites: Prior familiarity with basics of machine learning and a scripting language are helpful.

Benefits
This course enables the attendee to:

• Become familiar with deep learning concepts and applications.
• Understand how deep learning methods, specifically convolutional neural networks and recurrent neural networks work.
• Learn how to build, test, and improve the performance of deep networks using popular open- source utilities.

Course Description
Deep learning has revolutionized the machine learning community winning numerous competitions in computer vision and pattern recognition. Success in this space spans many domains including object detection, classification, speech recognition, natural language processing, action recognition, and scene understanding. In many cases, results surpass the abilities of humans. Activity in this space is pervasive, ranging from academic institutions to small startups to large corporations. This course encompasses the two hottest deep learning fields: convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and then gives the attendees hands-on training on how to build custom models using popular open source deep learning frameworks. CNNs are end-to-end, learning low level visual features and classifier simultaneously in a supervised fashion, giving substantial advantage over methods using independently solved features and classifiers. RNNs inject temporal feedback into neural networks. Transformer and Long Short Term Memory modules, are able to both remember long term sequences and forget more recent events. This course describes what deep networks are, how they evolved over the years, and how they differ from competing technologies. Examples are given demonstrating their widespread usage in imaging, and as this technology is described, indicating their effectiveness in many applications such as autonomous driving.

There are an abundance of approaches to getting started with deep learning, ranging from writing C++ code to editing scripts with the use of popular frameworks. After understanding how these networks are able to learn complex systems, the instructor gives instructions on how to setup your personal machine to install popular open source utilities and then demonstrates line-by-line how to build state-of-the-art models using Tensorflow 2.0 with Keras. The session concludes with tips and techniques for creating and training deep neural networks to perform classification on imagery, assessing performance of a trained network, and modifications for improved performance.

Intended Audience
Engineers, scientists, students, and managers interested in acquiring a broad understanding of deep learning.

Raymond Ptucha is a computational display technology leader in the Visual Experience Group at Apple where he is responsible for machine learning and algorithms in display products. He was an associate professor in computer engineering and director of the Machine Intelligence Laboratory at Rochester Institute of Technology (RIT) where he co-authored more than 100 publications including topics in machine learning, computer vision, and robotics, with a specialization in deep learning. Prior to RIT, Ptucha was a research scientist with Eastman Kodak Company where he worked on computational imaging algorithms and was awarded 33 US patents. He graduated from SUNY/Buffalo with a BS in computer science and a BS in electrical engineering. He earned a MS in image science from RIT. He earned a PhD in computer science from RIT (2013). Ptucha was awarded an NSF Graduate Research Fellowship in 2010 and his PhD research earned the 2014 Best RIT Doctoral Dissertation Award. Ptucha is a passionate supporter of STEM education, is an NVIDIA certified Deep Learning Institute instructor, chair of the Rochester area IEEE Signal Processing Society, and is an active member of his local IEEE chapter and FIRST robotics organizations.

Discounts given for multiple classes. See Registration Page for details to register.

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