Using TensorFlow and Long Short-Term Memory for Visualized Learning

by Sophia TurolApril 18, 2016

tensorflow-meetup-in-new-york-march-2016

Below are the videos from the TensorFlow New York meetup—sponsored and organized by Altoros on March 8, 2016.

 

TensorFlow essentials

In his session, Rafal Jozefowicz, a researcher at GoogleBrain, provided an overview of TensorFlow, focusing on the following:

  • The solution’s key features
  • TensorFlow core abstractions
  • How to assign devices to Ops with TensorFlow
  • Predefined / neural net specific Ops
  • Visualizing learning with TensorBoard
  • How to run a model in production with TensorFlow Serving
  • Case study: language modeling

 

 

Beyond LSTMs and visualized learning

Keith Davis of Metro-Nord Railroad provided the hitchhiker’s guide to TensorFlow. He mainly talked about image recognition, reinforcement learning, and Kohonen (self-organizing) maps. He also demonstrated how to implement recurrent neural networks and long short-term memory (LSTM) architecture in TensorFlow.

 

 

Fireside chat: TensorFlow adoption

After the talks delivered, Rafal Jozefowicz, Keith Davis, and Brandon Johnson shared their opinion on the following topics:

  • What makes TensorFlow stand out in a crowd as a tool?
  • How is TensorFlow applied within Google? How can it be used in other organizations?
  • How can the community push TensorFlow as a project?
  • How to attract more interest to TensorFlow?
  • Recommendations for those getting started with TensorFlow

 

Join our group to stay tuned with the upcoming meetups!

 

Further reading

Performance of Distributed TensorFlow
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