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Data Science 101: Recursive Deep Learning

In the talk below, Recursive Deep Learning for Modeling Compositional and Grounded Meaning, Richard Socher, Founder, MetaMind describes deep learning algorithms that learn representations for language that are useful for solving a variety of complex language tasks.

Statistics for Hackers

In this slide deck presentation below, Jake VanderPlas, discusses how you can use your coding skills to “hack statistics” – to replace some of the theory and jargon with intuitive computational approaches such as sampling, shuffling, cross-validation, and Bayesian methods.

Data Exploration with Databricks

The “Data Exploration on Databricks” jump start video below will show you how go from data source to visualization in a few easy steps. Specifically, you’ll see how to take semi-structured logs, easily extract and transform them, analyze and visualize the data using Spark SQL, so you can quickly understand your data.

Altiscale Increases Performance and Reliability of Hadoop and Spark Platform with Updated Altiscale Data Cloud

Altiscale, Inc., a leading provider of Big Data-as-a-Service, introduced Altiscale Data Cloud 4.0, featuring major upgrades to core Hadoop components, such as HDFS and YARN, and an expanded Spark-as-a-Service offering that supports all major versions of Apache Spark.

Leveraging Big Data to Gain a Competitive Advantage Across a Range of Industries

In this special technology white paper, Leveraging Big Data to Gain a Competitive Advantage Across a Range of Industries, you’ll see how organizations around the world are turning to the Hadoop data collection, management and analysis platform to optimize their efforts to extract value from big data.

Data Science 101: Deep Learning – Theory and Applications

Deep Learning is a hot topic in statistical learning and many data scientists are seeking a place to start. Here is a presentation from the July 23rd SF Machine Learning Meetup at the Workday Inc. San Francisco office. The featured speaker is Ilya Sutskever.

Five Data Science Predictions for 2016

The data science industry generated many headlines this year, from the U.S. Department of Commerce hiring its first chief data scientist to the National Science Foundation launching four regional data science brain trusts. But now that 2015 is winding down, it’s time to figure out where this buzzworthy industry is headed. Here are some predictions for next year from the CEO of DataScience.

Survey Reveals Enterprise Disconnect on Real-Time Streaming Data Analysis

VoltDB, a leading provider of an in-memory fast data platform to power mission-critical real-time applications, today announced the results of one of the industry’s first surveys to uncover the state of real-time data analysis in today’s enterprise. Conducted by independent panel research firm Research Now, the survey examines how different roles within the enterprise view real-time data capabilities and requirements.

Data Science for Social Good

Jake Porway is the founder and executive director of Datakind. In his Strata+Hadoop Keynote, Jake talks about data for the “best of intentions,” or using data to institute radical change to some of the world’s most pressing problems.

Dell Hadoop Solutions for Big Data

In this special technology assessment report, Dell Hadoop Solutions for Big Data, the premise is to unlock business critical insights from data of any type and size. Data growth is exploding, and analyzing large datasets—Big Data —has become the next frontier for innovation, competition, and productivity. IDC estimates that in 2012 alone, the amount of data created and replicated surpassed 2.8 zettabytes. One forecast from IDC estimates that data will grow to an overwhelming 44 zettabytes by 2020.