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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

Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches, including image recognition, NLP, predictions, & modeling.

  • Deep Learning in Practice

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe including use cases on machine translation, computer vision, & image recogition.

  • AI Meets the Physical World

    Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.

  • Papers to Production: CS in the Real World

    Groundbreaking papers make real world impact.

  • Solving Software Engineering Problems with Machine Learning

    Anomaly detection, ML in IDE's, bayesian optimization for config. Machine Learning techniques for more effective software engineering.

  • Predictive Architectures in the Real World

    Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.