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: Technical best practices sessions

11:55am–12:35pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial A
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.
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.
11:05am–11:45am Wednesday, September 20, 2017
Impact on business and society
Location: Yosemite A
Aaron Goldstein (Cylance)
Average rating: *****
(5.00, 3 ratings)
The current threat landscape is in a state of evolution that poses a significant risk to organizations' assets, reputations, and identities. Aaron Goldstein explores new and existing threats (and why traditional defenses fail to address them) and explains how leveraging AI techniques can improve the speed and efficiency of incident response tactics, even when combating the toughest threat actors. Read more.
1:45pm–2:25pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite BC
Lukas Biewald (Weights & Biases)
Average rating: ***..
(3.71, 7 ratings)
Making the best possible use of training data is essential for effective machine learning. Active learning can make your training data collection 10x–1,000x more efficient, while transfer learning opens up a world of new training data possibilities. Lukas Biewald explores the state of the art in training data, active learning, and transfer learning, especially as applied to deep learning. Read more.