TensorFlow in the Big Data Context

by Sophia TurolJune 14, 2016
Learn how deep learning libraries compare to each other, what distributed TensorFlow is, and how a graphics processing unit can accelerate model training.


TensorFlow is not the only machine learning library on the list, but it is gaining popularity by days. Below are the videos from Paris TensorFlow meetup—organized and sponsored by Altoros on March 23, 2016.


What makes TensorFlow different?

In her session, Jiqiong Qiu of SFEIR outlined the difference between academic research and an industry application, touched upon TensorFlow’s key features and how the tool stands out in a crowd, comparing the solution to other deep learning libraries. She also talked about distributed TensorFlow and Spark and demonstrated how GPU and multi-GPU accelerate model training in comparison to CPU.



You can find more details in Jiqiong’s full slides below.


TensorFlow overview

Romain Jouin of Mémorandum took a glimpse at TensorFlow, talking about its under-the-hood mechanisms, functionality, APIs, etc.



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Further reading


About the speakers

Jiqiong Qiu is Data Scientist at SFEIR. She holds a master’s degree in engineering and bioinformatics, as well as a PhD in data science. Jiqiong has experience in analyzing IoT data to detect abnormal behavior of domestic animals and working on a remote diagnostic system for skin diseases. Her current job is mainly focused on training deep neural networks using TensorFlow.


Romain Jouin is Senior Data Scientist at Mémorandum. He graduated from the University of Rouen with a master’s degree in computer science, as well as studied machine learning and data science at Télécom Paris. Romain has experience of working as Business Developer at Alcatel-Lucent and Toshiba. He currently specializes in big data and is focused on helping companies with data optimization.