Mobile and Embedded TensorFlow (TensorFlow Dev Summit 2017)
Did you know that TensorFlow models can be deployed in iOS and Android apps, and even run on Raspberry Pi? In this talk Pete Warden will go through everything you …
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Tag: TensorFlow
TensorFlow Ecosystem: Integrating TensorFlow with your infrastructure (TensorFlow Dev Summit 2017)
Generating input data, running distributed TensorFlow training, and serving models all involve other infrastructure components. Jonathan Hseu describes integration for each of these steps. Visit the TensorFlow website for all …
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Serving Models in Production with TensorFlow Serving (TensorFlow Dev Summit 2017)
Serving is the process of applying a trained model in your application. In this talk, Noah Fiedel describes TensorFlow Serving, a flexible, high-performance ML serving system designed for production environments. …
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ML Toolkit (TensorFlow Dev Summit 2017)
TensorFlow is an extremely powerful framework, yet has been missing packaged solutions that work out-of-the-box. In this talk, Ashish Agarwal introduces a toolkit of algorithms that takes a step in …
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Effective TensorFlow for Non-Experts (Google I/O ’17)
TensorFlow is Google’s machine learning framework. In this talk, you will learn how to use TensorFlow effectively. TensorFlow offers high level interfaces like Keras and Estimators, which can be used …
TensorFlow Frontiers (Google I/O ’17)
TensorFlow is an open-source machine learning (ML) platform that is fast, flexible, and production-ready. We’ll cover recent advances in the TensorFlow ecosystem with a focus on performance. See all the …
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Open Source TensorFlow Models (Google I/O ’17)
Come to this talk for a tour of the latest open source TensorFlow models for Image Classification, Natural Language Processing, and Computer Generated Artwork. Along the way, Josh Gordon will …
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Using Google Cloud and TensorFlow on Android Things (Google I/O ’17)
The talk will cover integration with Google Cloud and TensorFlow for machine learning and computer vision. See all the talks from Google I/O ’17 here: Watch more Android talks at …
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Android Meets TensorFlow: How to Accelerate Your App with AI (Google I/O ’17)
Portability is one of the main benefits of TensorFlow — you can easily move a neural network model to Android and run predictions on mobile phones, for all kinds of …
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From Research to Production with TensorFlow Serving (Google I/O ’17)
Learn how to bring your TensorFlow models from research to production. In this talk, Noah Fiedel describes how to export and serve your models at scale with TensorFlow Serving. He …
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