October 28–31, 2019
Schedule: Core technologies sessions
Hear direct from the TensorFlow project teams about the core projects in the TensorFlow ecosystem.
9:00am–5:00pm Tuesday, October 29, 2019
Location: Great American Ballroom J/K
Laurence Moroney (Google)
Average rating:
(3.50, 4 ratings)
Get a programmer's perspective on machine learning with Laurence Moroney, from the basics all the way up to building complex computer vision scenarios using convolutional neural networks and natural language processing with recurrent neural networks.
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1:30pm–5:00pm Tuesday, October 29, 2019
Location: Grand Ballroom A/B
Mars Geldard (University of Tasmania),
Tim Nugent (Lonely Coffee),
Paris Buttfield-Addison (Secret Lab)
Mars Geldard, Tim Nugent, and Paris Buttfield-Addison are here to prove Swift isn't just for app developers. Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries) and Swift—the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn deep learning and Swift.
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1:30pm–5:00pm Tuesday, October 29, 2019
Location: Grand Ballroom H
Martin Gorner (Google)
Average rating:
(5.00, 3 ratings)
Many problems deemed "impossible" only five years ago have now been solved by deep learning—from playing Go to recognizing what’s in an image to translating languages. Martin Gorner leads a hands-on introduction to recurrent neural networks and TensorFlow. Join in to discover what makes RNNs so powerful for time series analysis.
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11:00am–11:40am Wednesday, October 30, 2019
Location: Great American Ballroom J/K
Joshua Gordon (Google)
TensorFlow 2.0 is all about ease of use, and there has never been a better time to get started. Joshua Gordon walks you through three styles of model-building APIs, complete with code examples.
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11:50am–12:30pm Wednesday, October 30, 2019
Location: Great American Ballroom J/K
Average rating:
(5.00, 2 ratings)
Paige Bailey and Brennan Saeta walk you through Swift for TensorFlow, a next-generation machine learning platform that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional AI algorithms and general purpose software development.
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1:40pm–2:20pm Wednesday, October 30, 2019
Location: Great American Ballroom J/K
Robby Neale (Google)
Average rating:
(2.00, 1 rating)
There are many resources for building models from numeric data, which means processing text had to occur outside the model. Robby Neale walks you through ragged tensors and tf.text.
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2:30pm–3:10pm Wednesday, October 30, 2019
Location: Great American Ballroom J/K
Average rating:
(4.25, 4 ratings)
Join Taylor Robie and Priya Gupta to learn how you can use tf.distribute to scale your machine learning model on a variety of hardware platforms ranging from commercial cloud platforms to dedicated hardware. You'll learn tools and tips to get the best scaling for your training in TensorFlow.
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4:10pm–4:50pm Wednesday, October 30, 2019
Location: Great American Ballroom J/K
Average rating:
(5.00, 2 ratings)
Large-scale open source projects can be daunting, and one of the goals of TensorFlow is to be accessible to many contributors. Joana Carrasqueira and Nicole Pang share some great ways to get involved in TensorFlow, explain how its design and development works, and show you how to get started if you're new to machine learning or new to TensorFlow.
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5:00pm–5:40pm Wednesday, October 30, 2019
Location: Great American Ballroom J/K
Zak Stone (Google)
Average rating:
(4.00, 1 rating)
Join Zak Stone to see how researchers all over the world are expanding the frontiers of ML using free Cloud TPU capacity from the TensorFlow Research Cloud.
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11:00am–11:40am Thursday, October 31, 2019
Location: Great American Ballroom J/K
Average rating:
(3.50, 2 ratings)
ML development often focuses on metrics, delaying work on deployment and scaling issues. So Robert Crowe takes a deep dive into TensorFlow Extended.
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11:50am–12:30pm Thursday, October 31, 2019
Location: Great American Ballroom J/K
Average rating:
(4.00, 2 ratings)
Pete Warden, Nupur Garg, and Matthew Dupuy take you through TensorFlow Lite, TensorFlow’s lightweight cross-platform solution for mobile and embedded devices, which enables on-device machine learning inference with low latency, high performance, and a small binary size.
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1:40pm–2:20pm Thursday, October 31, 2019
Location: Great American Ballroom J/K
Raziel Alverez (Google)
Average rating:
(4.67, 3 ratings)
Raziel Alverez walks you through best current practices and future directions in core TensorFlow technology.
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2:30pm–3:10pm Thursday, October 31, 2019
Location: Great American Ballroom J/K
kangyi zhang (Google),
Brijesh Krishnaswami (Google),
Joseph Paul Cohen (Mila | University of Montreal),
Brendan Duke (ModiFace)
Kangyi Zhang, Brijesh Krishnaswami, Joseph Paul Cohen, and Brendan Duke dive into the TensorFlow.js ecosystem: how to bring an existing machine learning model into your JavaScript (JS) app, retrain the model with your data, and go beyond the browser to other JS platforms with live demos of models and featured apps (WeChat virtual plugin from L’Oréal and a radiology diagnostic tool from Mila).
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4:10pm–4:50pm Thursday, October 31, 2019
Location: Great American Ballroom J/K
Da-Cheng Juan (Google Research),
Sujith Ravi (Google AI)
Average rating:
(5.00, 1 rating)
Da-Cheng Juan and Sujith Ravi explain neural structured learning (NSL), an easy-to-use TensorFlow framework that both novice and advanced developers can use for training neural networks with structured signals.
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