October 28–31, 2019

Tutorials

These expert-led presentations on Tuesday, October 29 give you a chance to dive deep into the subject matter. Please note: to attend tutorials, you must register for a Gold or Silver pass; does not include access to training courses on Monday or Tuesday.

Learn more about a free 1-day tutorial, Zero to ML Hero with TensorFlow 2.0, available to Bronze pass and Learning pass holders.

Tuesday, October 29

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9:00am12:30pm
Location: Grand Ballroom H
Leonardo Apolonio (Clarabridge)
Leonardo Apolonio takes a deep dive into BERT and explains how you can use BERT to solve problems. Read more.
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9:00am12:30pm
Location: Grand Ballroom A/B
Sandeep Gupta (Google), Brijesh Krishnaswami (Google)
Join Sandeep Gupta and Brijesh Krishnaswami to learn how to build and deploy machine learning models using JavaScript, with official documentation, examples, and code labs from the TensorFlow team. Read more.
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9:00am12:30pm
Location: Grand Ballroom C/D
Robert Crowe (Google)
Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe outlines what's involved in creating a production ML pipeline and walks you through working code. Read more.
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9:00am5:00pm
Location: Great American Ballroom J/K
Laurence Moroney (Google)
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. Read more.
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9:00am12:30pm
Location: Grand Ballroom E
Andrew Selle (Google)
Andrew Selle offers an introduction to TensorFlow Lite and takes you through the conversion, performance, and optimization path while using Android and iOS applications. Read more.
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1:30pm5:00pm
Location: Grand Ballroom C/D
Neelima Mukiri and Meenakshi Kaushik demonstrate how to automate hyperparameter tuning for a given dataset using Katib and Kubeflow. Katib can be easily run on a laptop or in a distributed production deployment, and Katib jobs and configuration can be easily ported to any Kubernetes cluster. Read more.
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1:30pm5:00pm
Location: Grand Ballroom H
Martin Gorner (Google)
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. Read more.
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1:30pm5:00pm
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. Read more.
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1:30pm5:00pm
Location: Grand Ballroom G
Jason Mancuso (Dropout Labs), Yann Dupis (Dropout Labs)
Jason Mancuso and Yann Dupis demonstrate how to build and deploy privacy-preserving machine learning models using TF Encrypted, PySyft-TensorFlow, and the TensorFlow ecosystem. Read more.
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1:30pm5:00pm
Location: Grand Ballroom E
Maggie Zhang (NVIDIA), Nathan Luehr (NVIDIA), Josh Romero (NVIDIA), Pooya Davoodi (NVIDIA), Davide Onofrio (NVIDIA)
Maggie Zhang, Nathan Luehr, Josh Romero, Pooya Davoodi, and Davide Onofrio give you a sneak peek at software components from NVIDIA’s software stack so you can get the best out of your end-to-end AI applications on modern NVIDIA GPUs. They also examine features and tips and tricks to optimize your workloads right from data loading, processing, training, inference, and deployment. Read more.

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