Speed up Deep Learning with Distributed Training

May 31, 2018 · Mountain View, United States of America

Talk Abstract:
Developing and training distributed deep learning models at scale is challenging. We will show how to overcome these challenges and successfully build and train a distributed deep neural network with TensorFlow.

First, we will present deep learning on distributed infrastructure and cover various concepts such as experiment parallelism, model parallelism and data parallelism. Then, we will discuss limitations and challenges of each approach. Later, we will demonstrate hands on how to build and train distributed deep neural networks using TensorFlow GRPC (parameter and worker servers) on Clusterone.

Speaker Bio:
Babak Rasolzadeh https://www.linkedin.com/in/babakrasolzadeh/ got his PhD in Computer Vision and Robotics in 2010, after which he started his first company, OculusAI that he sold in 2013 to the world's largest media monitoring company, Meltwater. Since then Babak has held various Director roles in Data Science and Machine Learning in the industry, as well as been an advisor and investor in a handful of startups.

Agenda:
6:30–7 pm Meet and greet
7-8 pm Presentation and discussion
8-8:30pm Social and catch up

Event organizers
  • Silicon Valley AI Forum

    We are a professional organization for AI practitioners in the Silicon Valley. We aim to bring together data scientists, engineers, and business people working in AI and big data area. We host seminars, interactive group meetings, and mentoring sessions. We provide an exchange platform for big data professionals to share their experiences, learn about the newest technologies and explore potential startup opportunities. Join us today. Find like-minded people in AI and grow your career and AI and big data bu

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