Distributed Multi-Device Execution of TensorFlow for IoT
Below are the videos/slides from the TensorFlow Munich meetup—sponsored and organized by Altoros on March 1, 2016.
In his session, Ankit Bahuguna of Cliqz provided an insight into TensorFlow usage, the library’s mechanics, and implementation. To demonstrate TensorBoard’s capabilities in visualizing learning, he exemplified two demos on linear regression and visualizing word embeddings in TensorFlow. Finally, he shared the results of the recent benchmarks and outlined the likely scenarios of evolution.
Distributed multi-device execution of TensorFlow
Sebnem Rusitschka, Senior Key Expert for Cyber-physical Systems at Siemens, provided an overview of TensorFlow from a distributed computing perspective. She also elaborated on the perculiarities of embedded systems when using TensorFlow and highlighted the associated challenges. Finally, Sebnem talked about multi-dimensional IoT data, tensor networks, and arrays/indexing.
- What kind of tasks TensorFlow is built for?
- The industries / projects TensorFlow is applied within and the feedback received
- Recommendations for those getting started with TensorFlow
- The biggest challenges TensorFlow has at the moment
- Contributions of the community that can be of help
- The impact of open-sourcing TensorFlow
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- Visualizing TensorFlow Graphs with TensorBoard
- Distributed TensorFlow and Classification of Time-Series Data Using Neural Networks
- Adopting TensorFlow for Manufacturing and Industrial Internet of Things
About the speakers
Ankit Bahuguna is a Software Engineer at Cliqz, where he focuses on developing deep learning solutions to tackle the problem of low latency in web search. Ankit’s current work is focused on query embeddings, where the semantics of a user query is preserved in a fixed dimensional mathematical vector—trained using deep neural networks.
Sebnem Rusitschka has 10 years of experience in translating Internet-scale innovations to the world of industrial machines and processes. The area of her expertise includes digitized automation of infrastructures, especially, electrification. In 2015, she became Senior Key Expert for Cyber-Physical Systems at Siemens with a mission to bring together experts from various disciplines of machine learning, control theory, and distributed computing to achieve adaptive automation systems.