Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Schedule: Deep learning sessions

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9:00am - 5:00pm Sunday, September 17 & Monday, September 18
Location: Franciscan B
Mike Mendelson (NVIDIA)
Average rating: *****
(5.00, 1 rating)
NVIDIA Deep Learning Institute-certified instructor Mike Mendelson walks you through solving the most challenging problems with deep learning. You'll start with deep learning basic concepts and quickly move to taking on real-word problems using deep learning. Read more.
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9:00am–12:30pm Monday, September 18, 2017
Implementing AI
Location: Imperial A Level: Intermediate
Yufeng Guo (Google), Amy Unruh (Google)
Average rating: ***..
(3.33, 6 ratings)
Yufeng Guo and Amy Unruh walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng and Amy take you from conceptual overviews all the way to building complex classifiers and explain how you can apply deep learning to complex problems in science and industry. Read more.
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11:05am–11:45am Tuesday, September 19, 2017
Impact on business and society
Location: Imperial A Level: Intermediate
Xiaofang Chen (Pinterest), Derek Cheng (Pinterest)
Average rating: ****.
(4.62, 8 ratings)
Pinterest’s power is grounded in its personalization systems. Over the years, these recommender systems have evolved through different types of models. Xiaofang Chen and Derek Cheng explore Pinterest's recent transition from a GBDT system to one based in neural networks powered by TensorFlow, covering the challenges and solutions to providing recommendations to over 160M monthly active users. Read more.
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11:55am–12:35pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial A Level: Intermediate
Stephen Merity (Salesforce Research)
Average rating: ****.
(4.62, 8 ratings)
Deep learning is used broadly at the forefront of research, achieving state-of-the-art results across a variety of domains. However, that doesn't mean it's a fit for all tasks—especially when the constraints of production are considered. Stephen Merity investigates what tasks deep learning excels at, what tasks trigger a failure mode, and where current research is looking to remedy the situation. Read more.
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11:55am–12:35pm Tuesday, September 19, 2017
Verticals and applications
Location: Imperial B Level: Beginner
Blake Borgeson (Recursion Pharmaceuticals), Nan Li (Obvious Ventures)
Average rating: **...
(2.00, 1 rating)
Blake Borgeson and Nan Li offer a technical overview of how Recursion—a company that applies computer vision and machine learning to create a high-dimensional feature space in which to evaluate cellular health broadly across hundreds of disease states—leverages cellular phenotyping for drug discovery. Read more.
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1:45pm–2:25pm Tuesday, September 19, 2017
Implementing AI
Location: Yosemite A Level: Beginner
Siddha Ganju (Deep Vision)
Average rating: ***..
(3.33, 3 ratings)
Deep learning is necessary to bring intelligence and autonomy to the edge. Siddha Ganju offers an overview of Deep Vision's solution, which optimizes both the hardware and the software, and discusses the Deep Vision embedded processor, which is optimized for deep learning and computer vision and offers 50x higher performance per watt than existing embedded GPUs without sacrificing programmability. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial A Level: Beginner
Danny Lange (Unity Technologies)
Average rating: ****.
(4.00, 2 ratings)
Game development is a difficult and time-consuming pursuit that requires highly skilled labor to succeed. Drawing on his experience at Unity, Danny Lange demonstrates how deep learning and deep reinforcement learning can help developers at various stages in the development process create awesome digital experiences in gaming, VR, and AR. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Franciscan CD Level: Intermediate
Li Erran Li (Uber)
Average rating: ***..
(3.80, 5 ratings)
Deep reinforcement learning has enabled artificial agents to achieve human-level performance across many challenging domains (for example, playing Atari games and Go). Li Erran Li shares several important algorithms, discusses major challenges, and explores promising results. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial B Level: Intermediate
Jason Dai (Intel), Ding Ding (Intel)
Jason Dai and Ding Ding offer an overview of BigDL, an open source distributed deep learning framework built for big data platforms. By leveraging the cluster distribution capabilities in Apache Spark, BigDL successfully unleashes the power of large-scale distributed training in deep learning, providing good performance, efficient scaling on large clusters, and good convergence results. Read more.
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4:00pm–4:40pm Tuesday, September 19, 2017
Interacting with AI
Location: Imperial A Level: Beginner
Michael B. Henry (Mythic)
Average rating: **...
(2.67, 3 ratings)
Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Verticals and applications
Location: Imperial A Level: Intermediate
Jisheng Wang (Aruba, a Hewlett Packard Enterprise Company)
Average rating: *****
(5.00, 1 rating)
Recently, both deep learning and the IoT have attracted tremendous attention. Jisheng Wang shares firsthand experience in applying deep learning to solving some real-world enterprise IoT problems (e.g., IoT device identification and IoT security) and outlines some challenges for deep learning in enterprise applications, along with suggestions to overcome them. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Implementing AI
Location: Yosemite BC Level: Intermediate
Bharadwaj Pudipeddi (NVXL Technology)
Bharadwaj Pudipeddi proposes a highly dense modular acceleration cluster completely disaggregated from generic servers in the data center that is specifically targeted for deep learning- and AI-related workloads. This cluster is scalable and lightweight (and devoid of Xeons) with the ability to run very deep neural networks through data and model parallelism for extreme performance. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Impact on business and society
Location: Franciscan AB Level: Non-technical
Tim Estes (Digital Reasoning)
As AI moves from concept to reality, debates about ethics are evolving into excitement and the desire to learn more about AI and its promise of a better world. Tim Estes discusses two customer use cases at Digital Reasoning: Nasdaq, which found a way to use AI to help safeguard financial markets, and Thorn, which found a way to use AI to combat human trafficking and rescue children. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial B Level: Beginner
Kenneth Stanley (Uber AI Labs | University of Central Florida)
Average rating: ****.
(4.62, 8 ratings)
Kenneth Stanley offers an overview of the field of neuroevolution, an emerging paradigm for training neural networks through evolutionary principles that has grown up alongside more conventional deep learning, highlighting major algorithms such as NEAT, HyperNEAT, and novelty search, the field's emerging synergies with deep learning, and promising application areas. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Impact on business and society
Location: Franciscan AB Level: Beginner
Paul Tepper (Nuance Communications)
Average rating: ****.
(4.67, 3 ratings)
Many industries are now exploring chatbots powered by artificial intelligence as a source for improved insights and better understanding of customer preferences. Paul Tepper explores the unique challenges chatbots present, shares available solutions, and outlines a number of critical factors in building successful chatbots and virtual assistants. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite A Level: Beginner
Anirudh Koul (Microsoft)
Average rating: *****
(5.00, 2 ratings)
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in computer vision. Anirudh Koul explains how to bring the power of deep learning to memory- and power-constrained devices like smartphones. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial A Level: Beginner
Average rating: ****.
(4.00, 1 rating)
Current driving policy models are limited to models trained using homogenous data from a small number of vehicles running in controlled environments. Bruno Fernandez-Ruiz offers an overview of a network of connected devices that is building an end-to-end driving policy to leverage the 10 trillion miles driven every year. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial B Level: Intermediate
Nigel Toon (Graphcore)
Average rating: *****
(5.00, 1 rating)
Nigel Toon explains how new processing platforms will enable the next wave of machine intelligence beyond deep learning and how these machine learning innovations will impact businesses and improve competitiveness. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial A Level: Intermediate
Rachel Thomas (fast.ai)
Average rating: **...
(2.00, 6 ratings)
If the math used in AI seems intimidating, this tutorial is for you. Rachel Thomas walks you through working with arrays of different dimensions and how broadcasting handles data dimensions. You'll also gain hands-on experience with PyTorch, the Python framework for GPU computing developed by Facebook. Read more.
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2:35pm–3:15pm Wednesday, September 20, 2017
Implementing AI
Location: Grand Ballroom Level: Beginner
Yishay Carmiel (IntelligentWire)
Average rating: ****.
(4.00, 1 rating)
Today almost every achievement in language understanding is based on neural networks. Yishay Carmiel explains why analyzing conversational speech is still a challenging proposition despite all the recent breakthroughs in natural language processing and offers some potential solutions. Read more.
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4:00pm–4:40pm Wednesday, September 20, 2017
Implementing AI
Location: Grand Ballroom Level: Beginner
Wee Hyong Tok (Microsoft), Joy Qiao (Microsoft)
Average rating: *****
(5.00, 1 rating)
Joy Qiao and Wee Hyong Tok demonstrate how to combine Kubernetes clusters and deep learning toolkits to get the best of both worlds and jumpstart the development of innovative deep learning applications. Along the way, Joy and Wee Hyong explain how to train deep neural networks using GPU-enabled containers orchestrated by Kubernetes with common deep learning toolkits, such as CNTK and TensorFlow. Read more.
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4:50pm–5:30pm Wednesday, September 20, 2017
Impact on business and society
Location: Grand Ballroom Level: Intermediate
Nikita Lytkin (Facebook)
Average rating: ****.
(4.50, 2 ratings)
Nikita Lytkin explains how Facebook uses machine learning technologies developed by its ads ranking, applied machine learning, and AI research teams to enable personalized ecommerce that recommends a vast diversity of products to nearly two billion people. Read more.
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4:50pm–5:30pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite BC Level: Beginner
Sherry Moore (Google)
Average rating: ****.
(4.00, 2 ratings)
TensorFlow is the world's most popular machine learning framework. Google Brain team member Sherry Moore discusses the latest developments in TensorFlow and offers a dive into her research on evolving deep learning models using genetic algorithms. Read more.