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Deep Learning Using TensorFlow and TensorFlow-Slim

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Webinar December 21st_Presentation_Tensorflow

December 21, 2016 @ 1:00 pm - 2:00 pm

A Convolutional Neural Network (CNN) is a powerful machine learning technique from the field of deep learning.

Do you want learn how CNNs work and how to build and train such networks?

Watch the video to:

  • Learn more about the fundamentals of deep learning, followed by the strengths of using TensorFlow
  • Look about image classification using CNNs for MNIST dataset
  • Discover how CNNs work and how to build and train such networks
  • Examine how TensorFlow can be used for large-scale application of deep learning to big datasets in industry
In this webinar, Dipendra Jha, Ph.D. student in Computer Science from Northwestern University, provided a brief introduction to Deep Learning and TensorFlow, followed by actual implementation and demonstration of MNIST image classification using convolutional neural networks (CNNs).

Agenda:

  • Introduction to the Fundamentals of Deep Learning
  • The Strengths of Using TensorFlow
  • Image Classification Using CNNs for MNIST Dataset
  • How CNNs work and How to Build and Train Such Networks
  • The Usage of TensorFlow for large-scale application of Deep Learning to Big Datasets in Industry
  • Q&A
The slides are available here.

Who should watch the video:

This webinar will be of interest to Data Scientists, Software Engineers and Entrepreneurs in the areas of Connected Cars, Internet of Things/Industrial Internet, Medical Devices, Financial Technology (blockchain) and predictive apps/APIs of all sorts.

About the presenter:

Dipendra Jha is is a fourth-year Ph.D. student in Computer Science from Northwestern University. He is exploring the field of Deep Learning and Machine Learning using High Performance Computing (HPC) systems in the CUCIS lab under Prof. Alok Choudhary. His research focuses on scaling up deep learning and machine learning models using HPC (CPU and GPU) clusters, and their application to Material Science and Social Media Analytics.


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