Soumith Chintala introduces deep learning, what it is, why it has become popular, and how it can be fitted into existing machine learning solutions.
Sean Owen introduces Spark, Scala and random decision forests, and demonstrates the process of analyzing a real-world data set with them.
Evelina Gabasova explains how to run a social network analysis on Twitter and how to use data science tools to find out more about followers.
Thore Thomassen shares from experience how to combine structured data in a DWH with unstructured data in NoSQL, and using parallel data warehouse appliances to boost the analytical capabilities.
Peter Harrington explains what you do with machine learning, and what are the building blocks for an application that uses machine learning from collected data to creating predictions for customers.
Jonathan Bell & Gail Kaiser introduce Phosphor, a dynamic taint tracking system for the JVM, describing the approach used to achieve portable taint tracking.
Bob Kelly presents case studies on how Platfora uses Hadoop to do analytics for several of their customers.
Seth Juarez shares insight on how to create applications that use dashboards to drive value, convert raw data into answers, and simplify business processes.
The authors explain how the Pivotal team leveraged familiar SQL-based queries to analyze fine-grained cluster utilization using Spring XD.
Steve Hoffman, Ken Dallmeyer share their experience integrating Hadoop into the existing environment at Orbitz, creating a reusable data pipeline, ingesting, transporting, consuming and storing data.
Wesley Chow presents Chartbeat's real-time analytics platform and how able to handle the requests in a cost efficient manner using a custom written analytics engine in C and Lua.
Bryan Nehl makes an introduction to the data science: data formats, ETL tools, NoSQL databases, languages, libraries, techniques and approaches for exploring data and extracting value from it.