Using Multi-Threading to Build Neural Networks with TensorFlow and Apache Spark
At the recent TensorFlow meetup in Paris, the attendees learnt how to construct neural networks employing the multi-threading approach. The topics under discussion also included distributed TensorFlow and the efficiency of using Apache Spark with TensorFlow.
TensorFlow and deep learning without a PhD
Martin Gorner of Google introduced the attendees to TensorFlow and taught how to choose the right neural network for a specific problem. He demonstrated how to train the network with just a couple of code lines and a bag of “tricks of the trade.”
Constructing neural networks using multi-threading
In his session, Maxence Queyrel of Quinten talked about multi-threading with TensorFlow and Apache Spark, highlighting:
- Neural networks hyperparameters
- How TensorFlow works with Apache Spark (examples, efficiency, etc.)
- Distributed TensorFlow: What it is, how it works, and how to use it
TensorFlow + Apache Spark
Jiqiong Qiu of SFEIR provided an overview of H2O—an open-source machine learning platform—explaining why and how to use it together with TensorFlow and Apache Spark. With H2O, users can get the best of Apache Spark (its APIs, RDD, simple context, multi-tenancy, etc.) and combine it with deep learning algorithms.
Join the meetup group to stay informed about the upcoming events.
- Introduction to Neural Networks and Meta-Frameworks for Deep Learning with TensorFlow
- Distributed TensorFlow and Classification of Time Series Data Using Neural Networks
- Performance of Distributed TensorFlow: A Multi-Node and Multi-GPU Configuration
About the speakers