Using TensorFlow and Long Short-Term Memory for Visualized Learning

by Sophia TurolApril 18, 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