Presentation: TensorFlow: Pushing the ML Boundaries

Track: Deep Learning Applications & Practices

Location: Cyril Magnin I

Duration: 10:55am - 11:45am

Day of week: Wednesday

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Abstract

Google has made extraordinary advances in Machine Learning (ML) over the past few years. These breakthroughs requires enormous amounts of computation, both to train as well as run the underlying machine learning models. That's why we've built and deployed the Tensor Processing Unit (TPU), to allow us to support larger and larger amounts of ML workloads. In this talk, we'll look at the architecture of the TPU, how you write effective TensorFlow code to utilize its power, and how Google is using it to break new frontiers in Machine Learning.

Speaker: Magnus Hyttsten

TensorFlow Developer Advocate @Google

Magnus Hyttsten is a Developer Advocate for TensorFlow @ Google. He works on developing the TensorFlow product, is a developer fanatic, and an appreciated speaker at major industry events such as Google I/O, The AI Summit, AI Conference, ODSC, GTC, QCon, and others on machine learning and mobile development . Right now, he is focusing on Reinforcement Learning models, as well as making model inference effective on Mobile.

Find Magnus Hyttsten at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.