Sep 9–12, 2019

Schedule: Deep Learning tools sessions

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9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: 111
Robert Schroll (The Data Incubator)
The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.
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9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: 112
Rich Ott (The Data Incubator)
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Get the knowledge you need to build deep learning models using real-world datasets and PyTorch with Rich Ott. Read more.
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9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: Santa Clara
Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev detail a practical next step in DL learning with instructions, demos, and hands-on labs. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 C/D
Skyler Thomas (MapR)
The popular open source Kubeflow project is one of the best ways to start doing machine learning and AI on top of Kubernetes. However, Kubeflow is a huge project with dozens of large complex components. Skyler Thomas dives into the Kubeflow components and how they interact with Kubernetes. He explores the machine learning lifecycle from model training to model serving. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: Almaden Ballroom
Jason Dai (Intel), Yuhao Yang (Intel), Jiao(Jennie) Wang (Intel), Guoqiong Song (Intel)
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 E/F
Lukas Biewald (Weights & Biases)
Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (GRUs), and other text classification techniques using Keras and scikit-learn. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: Almaden Ballroom
Mo Patel (Independent)
PyTorch captured the minds of ML researchers and developers upon its arrival. Now it's matured into a production-ready ML framework with use cases and applications. Mo Patel explores the PyTorch lifecycle via hands-on examples such as image and text classification and linear modeling. You'll cover other aspects of ML such as transfer learning, data modeling, and deploying to production in labs. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: LL21 A/B
Neil Conway (Determined AI), Yoav Zimmerman (Determined AI)
Success with DL requires more than just TensorFlow or Keras. Neil Conway and Yoav Zimmerman detail a range of practical problems faced by DL practitioners and the software tools and techniques you'll need to address the problems, including data prep and augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile and edge optimization. Read more.
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11:05am11:45am Wednesday, September 11, 2019
Location: 230 C
Urs Köster (Cerebras Systems)
Session by Urs Köster, head of machine learning at Cerebras Systems. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: Expo Hall 3
Hagay Lupesko (Facebook)
Hagay Lupesko discusses AI-powered personalization at Facebook: the challenges and practical techniques applied to overcome these challenges. You will learn about deep learning based personalization modeling, scalable training, and the accompanying system design approaches that are applied in practice. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: LL21 C/D
Michael Bauer (Sylabs, Inc.)
Containerization technology can build distributed, scalable, and complex neural networks by leveraging decoupled resource pools—pools that would not traditionally be amenable to such a task. Using Singularity, Michael Bauer demonstrates the approach of treating a container as a decoupled neural interface (DNI) to enable novel applications for neural networks that were previously impractical. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: Expo Hall 3
Paige Bailey (Google)
TensorFlow 2.0 has landed. Paige Bailey walks you through TensorFlow (TF) 2.0's new features, usability enhancements, performance increases, and focus on developer productivity. You'll use the TF 2.0 migration tool to transition a model from TensorFlow 1.x to 2.0 and deploy an end-to-end open source machine learning model. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: LL21 C/D
Evan Sparks (Determined AI)
Evan Sparks walks you through the current gap between the AI haves (Google, Facebook, Amazon, and Microsoft) and the AI have-nots (the rest of the industry), from the perspective of software infrastructure for model development. You'll learn some of the opportunities for end-to-end system design to enable rapid iteration and scale in AI application development. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: Expo Hall 3
Joseph Spisak (Facebook), Hao Lu (Facebook)
Learn how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook with Joseph Spisak and Hao Lu. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: Expo Hall 3
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Siddha Ganju and Meher Kasam examine optimizing deep neural nets to run efficiently on mobile devices. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: 230 A
Mathew Salvaris (Microsoft), Angus Taylor (Microsoft)
Join Danielle Dean and Wee Hyong Tok to learn best practices and reference architectures (which have been validated in real-world AI/ML projects for customers globally) for implementing AI. Wee Hyong and Danielle detail lessons distilled from working with large global customers on AI/ML projects and the challenges that they overcame. Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: 230 C
Shashank Prasanna (Amazon Web Services)
Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: Expo Hall 3
Brennan Saeta (Google)
Swift for TensorFlow is a next-generation machine learning and differential programming framework that unlocks new domains and applications. Brennan Saeta leads you through the motivations for Swift, the benefits of this toolchain, and how to use Swift for TensorFlow in your projects. Read more.

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