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: Tools and frameworks sessions

9:00am–12:30pm Monday, September 18, 2017
Implementing AI
Location: Imperial A
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.
1:30pm–5:00pm Monday, September 18, 2017
Implementing AI
Location: Imperial B
Kristian Hammond (Northwestern Computer Science)
Average rating: ****.
(4.68, 22 ratings)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making. Read more.
1:30pm–5:00pm Monday, September 18, 2017
Implementing AI
Location: Yosemite A
Gunnar Carlsson (Ayasdi)
Average rating: *****
(5.00, 1 rating)
Topological data analysis (TDA) is a framework for machine learning that synthesizes and combines machine learning algorithms to identify the shape of data. The technique is responsible for several major breakthroughs in our understanding of science and business. Gunnar Carlsson offers an overview of TDA's mathematical underpinnings and its practical application through software. Read more.
11:55am–12:35pm Tuesday, September 19, 2017
Implementing AI
Location: Yosemite BC
Ion Stoica (University of California, Berkeley)
Average rating: ***..
(3.75, 4 ratings)
Ion Stoica offers an overview of Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms. Read more.
2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial A
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.
2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial B
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.
4:00pm–4:40pm Tuesday, September 19, 2017
Implementing AI
Location: Grand Ballroom
Jeremy Howard ( fast.ai | USF | doc.ai and platform.ai)
Average rating: ****.
(4.00, 1 rating)
Although most devs are aware of the benefits of GPU acceleration, many assume that the technique is only applicable to specialist areas like deep learning and that learning to program a GPU takes complex specialist knowledge. Jeremy Howard explains how to easily harness the power of a GPU using a standard clustering algorithm with PyTorch and demonstrates how to get a 20x speedup over NumPy. Read more.
4:50pm–5:30pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial B
Mary Wahl (Microsoft)
Average rating: ****.
(4.00, 1 rating)
Mary Wahl shares a cloud-based Hadoop ecosystem solution for deploying deep neural networks (DNNs) with scalable compute resources to accommodate changing workloads and demonstrates how to apply trained Microsoft CNTK and TensorFlow DNNs to a large image set in HDFS (Azure Data Lake Store) using the Python bindings for these deep learning frameworks and a Microsoft HDInsight Spark cluster. Read more.
11:55am–12:35pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial B
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.
1:45pm–2:25pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite A
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.
2:35pm–3:15pm Wednesday, September 20, 2017
Implementing AI
Location: Franciscan AB
Nate Soares (MIRI)
The field of artificial intelligence has made major strides in recent years, but there is a growing movement to consider the implications of machines that can rival humans in general problem-solving abilities. Nate Soares outlines the underresearched fundamental technical obstacles to building AI that can reliably learn to be "aligned" with human values. Read more.
2:35pm–3:15pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial A
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.