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Teaching Recurrent Neural Networks Using TensorFlow


August 31, 2016 @ 11:00 am - 12:00 pm

Recurrent neural networks (RNNs) are designed to model sequential information and are widely used to solve the problems of speech recognition, language modeling, translation, and image captioning.

Are you willing to learn how to perform basic mathematical calculations or recognize handwriting using RNNs and TensorFlow together?

Why watch the video:

  • Discover how recurrent neural networks operate.
  • Learn the prime reasons for choosing an RNN rather than a standard network
  • Walk through code in TensorFlow for modeling a sine wave, performing basic addition, and generating handwriting
In this session, Rajiv Shah, Adjunct Assistant Professor at University of Illinois, provided a brief introduction to recurrent neural networks.


  • How Recurrent Neural Networks are used
  • Tensorflow Playground
  • Learn a Sine Wave
  • Learn to Add
  • Learning Handwriting
  • Q&A
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:

Rajiv Shah is a data scientist at a Global Supply Network Division and an Adjunct Assistant Professor at the University of Illinois at Chicago. He is an active member of the data science community in Chicago with projects and publications related to surveillance and red light cameras. He has a PhD from the University of Illinois at Urbana Champaign.

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