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IEEE Workshops on Machine Learning, Convolutional Neural Networks, and Tensorflow

September 24, 2018 @ 4:00 pm - September 25, 2018 @ 9:00 pm PDT

Speaker: Dr. Kiran Gunnam, IEEE Distinguished Speaker and Distinguished Engineer – Machine Learning & Computer Vision at Western Digital


Workshop 1: Machine Learning Foundations -An Intuitive Approach

This first workshop offers an intuitive treatment of the important machine learning approaches. The workshop covers supervised Learning and unsupervised learning. Various classic machine learning as well as modern deep neural networks and deep belief networks are covered. How to build an end-to-end application is covered in depth focusing on selecting right machine learning algorithm, data preprocessing and evaluating model.

Workshop 2: Deep learning with CNN and Tensorflow

This second workshop offers an in-depth treatment of Convolutional neural networks (CNN) and explain each layer in detail. It also covers various architecture optimization techniques including data optimization, drop outs, layer patterns and sizing. It provides a comprehensive case study of recent CNN architectures including AlexNet, ZFNet and GoogleNet.

Tensorflow basics would be covered. Guided exercises in Tensorflow involve programming linear regression and nearest neighbors approaches, building a simple neural network for XOR, building CNN for handwritten digits recognition. Template software functions are provided with most of the software is written except for the key concepts. Instructor will work with attendees to help them complete the solutions.

For the detailed list of topics covered, please see the link.


Course slides in PDF and other workshop materials will be shared with registered attendees 5-days before the course. In addition, workshop materials with Tensorflow installation are provided also as docker image to have a worry free setup. Attendees for workshop 2 should bring their own laptop prepared with provided docker image or Tensorflow+provided examples.

Biography: Dr. Kiran Gunnam is an innovative technology leader with vision and passion who effectively connects with individuals and groups. Dr. Gunnam’s breakthrough contributions are in the areas of advanced error correction systems, storage class memory systems and computer vision based localization & navigation systems. He has helped drive organizations to become industry leaders through ground-breaking technologies. Dr. Gunnam has 75 issued patents and 100+ patent applications/invention disclosures on algorithms, architectures and real-time low-cost implementations for computing, storage and computer vision systems. He is the lead inventor/sole inventor for 90% of them. Dr. Gunnam’s patented work has been already incorporated in more than 2 billion data storage and WiFi chips and is set to continue to be incorporated in more than 500 million chips per year. Dr. Gunnam is also a key contributor to the precise localization and navigation technology commercialized for autonomous aerial refueling and space docking applications. His recent patent pending inventions on low-complexity simultaneous localization and mapping (SLAM) and 3D convolutional neural network (CNN) for object detection, tracking and classification are being commercialized for LiDAR+camera based perception for autonomous driving and robotic systems. Dr. Gunnam received his MSEE and PhD in Computer Engineering from Texas A&M University, College Station. He is world-renowned for balance between strong analytical ability and pragmatic insight into implementation of advanced technology. He served as IEEE Distinguished Speaker and Plenary Speaker for 25+ events and international conferences and more than 3000 attendees in USA, Canada and Asia benefited from his lecture talks. He also teaches graduate level course focused on machine learning systems at Santa Clara University.


IEEE SCV Computational Intelligence Society
IEEE SCV – Communications Society (ComSoc)
Apollo AI


Texas Instruments Building E Conference Center

2900 Semiconductor Drive
Santa Clara, CA United States