Presented By O'Reilly and Cloudera
December 5-6, 2016: Training
December 6–8, 2016: Tutorials & Conference

Ai conference sessions

11:15am–11:55am Thursday, 12/08/2016
With the rise of deep learning, natural language understanding techniques are becoming more effective and are not as reliant on costly annotated data. This leads to an explosion of possibilities of what businesses can do with language. Alyona Medelyan explains what the newest NLU tools can achieve today and presents their common use cases.
5:05pm–5:45pm Wednesday, 12/07/2016
Matt Winkler and Jennifer Marsman explain how to easily extend your apps and services with bots to reach users where they are—in messaging apps—covering use cases and case studies, how to quickly get started building a bot, how to process input using linguistic analysis, and how to deploy and integrate bots with messaging apps.
2:35pm–3:15pm Thursday, 12/08/2016
Deep learning has made a huge impact on predictive analytics and is here to stay, so you'd better get up to speed with the neural net craze. Mateusz Dymczyk explains why all the top companies are using deep learning, what it's all about, and how you can start experimenting and implementing deep learning solutions in your business in only a few easy steps.
5:05pm–5:45pm Thursday, 12/08/2016
Ever wondered how Google Translate works so well, how the autocaptioning works on YouTube, or how to mine the sentiments of tweets on Twitter? What’s the underlying theme? They all use deep learning. Bargava Subramanian and Amit Kapoor explore artificial neural networks and deep learning for natural language processing to get you started.
1:30pm–5:00pm Tuesday, 12/06/2016
Wolff Dobson walks you through training and deploying a machine-learning system using TensorFlow, a popular open source library, and demonstrates how to build machine-learning systems from simple classifiers to complex image-based models.
4:15pm–4:55pm Thursday, 12/08/2016
Adam Gibson offers a brief overview of deep reinforcement learning on Spark, exploring how to run large-scale training on Spark and the implications on deep reinforcement learning targeting the doom environment.
2:35pm–3:15pm Wednesday, 12/07/2016
Nir Lotan describes a new, free software tool based on existing deep learning frameworks that enables the fast and easy creation of deep learning models and incorporates extensive optimizations that provide high performance on standard CPUs.
1:45pm–2:25pm Thursday, 12/08/2016
When operating on billions of data events per day, modern AI and machine-learning programs require distributed clusters with tens to hundreds machines. Qirong Ho offers an introduction to high-efficiency AI and ML distributed systems developed as part of the Petuum open source project and explains how they can reduce capital and operational costs for businesses.
10:15am–10:35am Thursday, 12/08/2016
M. C. Srivas covers the technologies underpinning the big data architecture at Uber and explores some of the real-time problems Uber needs to solve to make ride sharing as smooth and ubiquitous as running water, explaining how they are related to real-time big data analytics.
10:00am–10:15am Wednesday, 12/07/2016
Machine learning and artificial intelligence show great promise, but, really, machine learning and deep learning are already here and being used everywhere around you. Find out how Google uses large-scale machine learning in many of its products, and how TensorFlow and ML can help your business (and even help you make art and music).
4:15pm–4:55pm Thursday, 12/08/2016
Anusua Trivedi proposes a method to apply a pretrained deep convolution neural network (DCNN) on images to improve prediction accuracy. This approach improves prediction accuracy on domain-specific image datasets compared to state-of-the-art machine-learning approaches.