Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Schedule: Artificial Intelligence sessions

9:00am12:30pm Tuesday, September 26, 2017
Location: 1A 18 Level: Intermediate
Secondary topics:  Deep learning, ecommerce
Mo Patel (Independent), Junxia Li (Think Big Analytics)
Junxia Li and Mo Patel demonstrate how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest for recommendations using embeddings. You'll also learn how to achieve wide and deep learning with WALS matrix factorization—now used in production for the Google Play store. Read more.
9:00am5:00pm Tuesday, September 26, 2017
Location: 1A 06/07
Ben Lorica (O'Reilly), Assaf Araki (Intel), Jacob Schreiber (University of Washington), Alex Ratner (Stanford University), Madeleine Udell (Cornell University), Yunsong Guo (Pinterest), Katherine Heller (Duke University), Alan Nichol (Rasa), Gerard de Melo (Rutgers University), Tamara Broderick (MIT), Inbal Tadeski (Anodot), Daniel Kang (Stanford University), Bichen Wu (UC Berkeley), Shaked Shammah (Hebrew University)
A full day of hardcore data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. Along the way, leading data science practitioners teach new techniques and technologies to add to your data science toolbox. Read more.
1:30pm5:00pm Tuesday, September 26, 2017
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Deep learning, Healthcare
Josh Patterson (Patterson Consulting), Vartika Singh (Cloudera), Dave Kale (Skymind), Tom Hanlon (Functional Media)
Average rating: **...
(2.00, 1 rating)
Josh Patterson, Vartika Singh, David Kale, and Tom Hanlon walk you through interactively developing and training deep neural networks to analyze digital health data using the Cloudera Workbench and Deeplearning4j (DL4J). You'll learn how to use the Workbench to rapidly explore real-world clinical data, build data-preparation pipelines, and launch training of neural networks. Read more.
1:15pm1:55pm Wednesday, September 27, 2017
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Deep learning
Yuhao Yang (Intel), Zhichao Li (Intel)
Average rating: ****.
(4.00, 2 ratings)
Yuhao Yang and Zhichao Li discuss building end-to-end analytics and deep learning applications, such as speech recognition and object detection, on top of BigDL and Spark and explore recent developments in BigDL, including Python APIs, notebook and TensorBoard support, TensorFlow model R/W support, better recurrent and recursive net support, and 3D image convolutions. Read more.
4:35pm5:15pm Wednesday, September 27, 2017
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Financial services, Platform
Nadeem Gulzar (Danske Bank Group), Sune Askjær (Think Big Analytics, a Teradata Company)
Average rating: *****
(5.00, 3 ratings)
Fraud in banking is an arms race, and criminals are now using machine learning to improve their attack effectiveness. Sune Askjaer and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection, covering model effectiveness, TensorFlow versus boosted decision trees, operational considerations in training and deploying models, and lessons learned along the way. Read more.
5:25pm6:05pm Wednesday, September 27, 2017
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Cloud, Deep learning
Leo Dirac (Amazon Web Services)
Average rating: *****
(5.00, 6 ratings)
Leo Dirac demonstrates how to apply the latest deep learning techniques to semantically understand images. You'll learn what embeddings are, how to extract them from your images using deep convolutional neural networks (CNNs), and how they can be used to cluster and classify large datasets of images. Read more.
1:15pm1:55pm Thursday, September 28, 2017
Location: 1A 12/14 Level: Beginner
Secondary topics:  AI
Richard Tibbetts (Tableau)
Average rating: **...
(2.75, 4 ratings)
Businesses have spent decades trying to make better decisions by collecting and analyzing structured data. New AI technologies are beginning to transform this process. Richard Tibbetts explores AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer questions in minutes that previously took trained statisticians hours. Read more.