TensorFlow in Action: TensorBoard, Training a Model, and Deep Q-learning

by Sophia TurolMay 16, 2016


In data science, Q-learning represents an approach to learning about a state space and following the optimal policy thereafter. A classic example is a robot navigating a maze. Below are the videos from the TensorFlow London meetup featuring deep dive into TensorFlow’s architecture and a Q-learning example.


Technical overview of TensorFlow

In her session, Rebecca Murphy of Ocado Technology focused on TensorFlow under-the-hood mechanisms and demonstrated how to install the solution, load data, feed, checkpoint, and load the model. Then, she moved on to explaining what TensorBoard is and how to use it for visualizing learning. Finally, she talked about the further development of TensorFlow.



Deep Q-learning with TensorFlow and PyGame

In his presentation, Daniel Slater from Bank of America Merrill Lynch touched upon such topics as reinforcement learning and Q-learning and how to apply these techniques to implement the PyGame framework with TensorFlow.

The PyGame solution described by Daniel Slater was also presented at the PyData conference earlier this year. In addition to a detailed description and great live demo, Daniel talked on TensorFlow, neural networks, reinforcement learning, Q-learning, and convolutional networks.

For more details, read a blog post on the matter by Daniel Slater or check out this project on GitHub.


Fireside chat

On delivering the presentations, Daniel Slater, Rebecca Murphy, and Peter Morgan shared their vision on:

  • What are the main challenges that can be solved with TensorFlow?
  • The recommendations for those getting started with TensoFlow
  • How can TensorFlow be further improved? How can the community contribute?


Join our group to get informed about the upcoming meetups!


Further reading


About the speakers

Peter Morgan is a published author and computer science industry veteran with twenty years’ experience working within the IT industry. Before entering industry, he solved high energy physics problems while enrolled in the PhD program in physics at the University of Massachusetts at Amherst. After spending three years as a Research Associate on an experiment lead by Stanford University to measure the mass of the neutrino, Peter now works as a technical director at Data Science Partnership—a company he co-founded—overseeing business development and helping clients to design and implement their deep learning solutions.


Rebecca Murphy is a data scientist at Ocado Technology, where she uses deep learning techniques to understand the intricacies of customer choice. She recently completed a PhD at the University of Cambridge, where she spent four years playing with high-power lasers and developed Monte Carlo methods to analyse data from fluorescently labelled protein molecules. In her spare time, Rebecca enjoys running, swimming, cycling and reading. You can follow her on Twitter or GitHub.


Daniel Slater is an obsessive programmer who has worked across finance, computer games and e-commerce. Currently working as an AVP at Bank of America Merrill Lynch and doing an MRes in machine learning with a focus on reinforcement learning.
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