Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Schedule: Deep learning sessions

Deep learning has become extremely active that is quickly paving the way for modern machine learning. The following sessions cover practical techniques for getting started, including a hands-on introduction to TensorFlow — where you’ll learn to build and deploy simple and complex models, an introduction to context-dependent text generation — with an example using an interactive Twitter bot, and an overview of LIME — a technique to explain the predictions of any classifier in an interpretable manner.

9:00am - 5:00pm Monday, March 13 & Tuesday, March 14
Robert Schroll (The Data Incubator)
Average rating: ***..
(3.20, 5 ratings)
Robert Schroll demonstrates TensorFlow's capabilities through its Python interface and explores TFLearn, a high-level deep learning library built on TensorFlow. Join in to learn how to use TFLearn and TensorFlow to build machine-learning models on real-world data. Read more.
9:00am12:30pm Tuesday, March 14, 2017
Data science & advanced analytics
Location: LL21 E/F Level: Intermediate
Amy Unruh (Google), Yufeng Guo (Google)
Average rating: ***..
(3.69, 16 ratings)
Amy Unruh and Yufeng Guo walk you through training and deploying a machine-learning system using TensorFlow, a popular open source library. Amy and Yufeng begin by giving an overview of TensorFlow and demonstrating some fun, already-trained TensorFlow models. Read more.
9:00am5:00pm Tuesday, March 14, 2017
Location: LL20 B
Michael Abbott (Stanford University), Christopher Pouliot (Nio), Jennifer Anderson, Renee DiResta (New Knowledge), Coco Krumme (Haven | UC Berkeley), Ryan Baumann (Mapbox), JAVONA WHITE BEAR (IBM), Andre Luckow (BMW Group), Rajiv Paul (Yakit), Evangelos Simoudis (Synapse Partners), Roland Major (Transport for London), Rodrigo Fontecilla (Unisys), Lloyd Palum (Vnomics), Andreas Ribbrock (#zeroG, A Lufthansa Systems Company)
Data, Transportation, and Logistics Day offers a daylong deep-dive into how data science is changing transportation and logistics. We’ll investigate the latest advances in and applications of self-driving vehicles, automated drones, and embedded sensors and explore how new uses of data are challenging the industry to evolve infrastructure for the future. Read more.
9:00am5:00pm Tuesday, March 14, 2017
Location: LL20 A
Barbara Eckman (Comcast), Dirk Jungnickel (Emirates Integrated Telecommunications Company (du)), Kishore Papineni (Astellas Pharma), Paul Barth (Podium Data), Carlo Torniai (Pirelli Tyre), Bryan Harrison (American Express), Chris Murphy (Zurich Insurance Group), Martin Lidl (Deloitte), Maura Lynch (Pinterest), Nixon Patel (Kovid Group), Bas Geerdink (ING), Robin Li (Tapjoy), Yohan Chin (Tapjoy), Jim Harrold (NationBuilder), Lana Novikova (Heartbeat AI Technologies)
In a series of 12 half-hour talks aimed at a business audience, you’ll hear data-themed case studies from household brands and global companies, explaining the challenges they wanted to tackle, the approaches they took, and the benefits—and drawbacks—of their solutions. If you want practical insights about applied data, look no further. Read more.
9:00am12:30pm Tuesday, March 14, 2017
Location: LL20 C
Edd Wilder-James (Google), Ellen Friedman (MapR Technologies), Jim Scott (MapR Technologies), GABRIELA QUEIROZ (R-Ladies), Melanie Warrick (Google), Aneesh Karve (Quilt)
Data 101 introduces you to core principles of data architecture, teaches you how to build and manage successful data teams, and inspires you to do more with your data through real-world applications. Setting the foundation for deeper dives on the following days of Strata + Hadoop World, Data 101 reinforces data fundamentals and helps you focus on how data can solve your business problems. Read more.
1:30pm5:00pm Tuesday, March 14, 2017
Data science & advanced analytics
Location: LL20 D Level: Intermediate
Dave Kale (Skymind), Susan Eraly (Skymind), Josh Patterson (Skymind)
Average rating: ***..
(3.33, 3 ratings)
Dave Kale, Susan Eraly, and Josh Patterson explain how to build, train, and deploy neural networks using Deeplearning4j. Topics include the fundamentals of deep learning, ND4J and DL4J, and scalable training using GPUs and Apache Spark. You'll gain hands-on experience with several models, including convolutional and recurrent neural nets. Read more.
11:00am11:40am Wednesday, March 15, 2017
Data science & advanced analytics
Location: 210 C/G Level: Intermediate
Feng Zhu (Clobotics), Valentine Fontama (Microsoft)
Average rating: ****.
(4.71, 7 ratings)
Although deep learning has proved to be very powerful, few results are reported on its application to business-focused problems. Feng Zhu and Val Fontama explore how Microsoft built a deep learning-based churn predictive model and demonstrate how to explain the predictions using LIME—a novel algorithm published in KDD 2016—to make the black box models more transparent and accessible. Read more.
11:50am12:30pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 210 C/G Level: Intermediate
Anirudh Koul (Microsoft)
Average rating: ****.
(4.20, 5 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 and drones. Read more.
1:50pm2:30pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 230 C Level: Intermediate
David Talby (Pacific AI), Claudiu Branzan (Accenture AI)
Average rating: ****.
(4.14, 7 ratings)
David Talby and Claudiu Branzan offer a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records. Infrastructure components include Kafka, Spark Streaming, Spark, and Elasticsearch; data science components include spaCy, custom annotators, curated taxonomies, machine-learned dynamic ontologies, and real-time inferencing. Read more.
1:50pm2:30pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 210 C/G Level: Advanced
Michael Dusenberry (IBM Spark Technology Center), Frederick Reiss (IBM)
Average rating: *****
(5.00, 2 ratings)
Estimating the growth rate of tumors is a very important but very expensive and time-consuming part of diagnosing and treating breast cancer. Michael Dusenberry and Frederick Reiss describe how to use deep learning with Apache Spark and Apache SystemML to automate this critical image classification task. Read more.
2:40pm3:20pm Wednesday, March 15, 2017
James Bradbury (Salesforce Research)
Average rating: ****.
(4.00, 8 ratings)
James Bradbury offers an overview of PyTorch, a brand-new deep learning framework from developers at Facebook AI Research that's intended to be faster, easier, and more flexible than alternatives like TensorFlow. James makes the case for PyTorch, focusing on the library's advantages for natural language processing and reinforcement learning. Read more.
4:20pm5:00pm Wednesday, March 15, 2017
Data science & advanced analytics, Real-time applications
Location: 210 C/G Level: Intermediate
Shivnath Babu (Duke University | Unravel Data Systems)
Average rating: ***..
(3.33, 3 ratings)
Shivnath Babu offers an introduction to using deep learning to solve complex problems in IT operations analytics. Shivnath focuses on how deep learning can derive operations insights automatically for the complex big data application stack composed of systems such as Hadoop, Spark, Cassandra, Elasticsearch, and Impala, using examples of open source tools for deep learning. Read more.
5:10pm5:50pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 210 C/G Level: Intermediate
Stephen Merity (Salesforce Research)
Average rating: ****.
(4.67, 3 ratings)
While attention and memory have become important components in many state-of-the-art deep learning architectures, it's not always obvious where they may be most useful. Even more challenging, such models can be very computationally intensive for production. Stephen Merity discusses the most recent techniques, what tasks they show the most promise in, and when they make sense in production systems. Read more.
11:00am11:40am Thursday, March 16, 2017
Rajat Monga (Google)
Average rating: ***..
(3.86, 7 ratings)
Rajat Monga offers an overview of TensorFlow progress and adoption in 2016 before looking ahead to the areas of importance in the future—performance, usability, and ubiquity—and the efforts TensorFlow is making in those areas. Read more.
11:00am11:40am Thursday, March 16, 2017
Anima Anandkumar (UC Irvine)
Average rating: ****.
(4.67, 3 ratings)
Anima Anandkumar demonstrates how to use preconfigured Deep Learning AMIs and CloudFormation templates on AWS to help speed up deep learning development and shares use cases in computer vision and natural language processing. Read more.
11:50am12:30pm Thursday, March 16, 2017
Spark & beyond
Location: 210 A/E
Joseph Bradley (Databricks), Tim Hunter (Databricks, Inc.)
Average rating: ***..
(3.75, 4 ratings)
Joseph Bradley and Tim Hunter share best practices for building deep learning pipelines with Apache Spark, covering cluster setup, data ingest, tuning clusters, and monitoring jobs—all demonstrated using Google’s TensorFlow library. Read more.
4:20pm5:00pm Thursday, March 16, 2017
Shivnath Babu (Duke University | Unravel Data Systems)
Average rating: *****
(5.00, 1 rating)
Shivnath Babu offers an introduction to using deep learning to solve complex problems in IT operations analytics. Shivnath focuses on how deep learning can derive operations insights automatically for the complex big data application stack composed of systems such as Hadoop, Spark, Cassandra, Elasticsearch, and Impala, using examples of open source tools for deep learning. Read more.