Are you involved with a business that uses open government data? If so, please register to become a part of Open Data 500 to make the list of 500 companies benefiting from open government data.
The Open Data 500 list is being compiled by The Governance Lab at New York University. The goal of the list is to identify how government data is being used in business and if the data can be used for innovation. So if you are involved or know a business using open government data, please register them for Open Data 500.
Recently, MIT Technology Review ran an article about the new uses of deep learning at Facebook. Facebook would like to use deep learning to understand more about its users. They have assembled quite a team.
If you are looking to learn more about deep learning, Andrew Ng, cofounder of Coursera, has some course materials on deep learning available on the Stanford Openclassroom site. The materials appear incomplete, but they do provide lectures covering neural networks which are the foundations of deep learning.
On October 16, 2013, Kalido will be hosting another data science webinar, Data Science: Not Just For Big Data. David Smith of Revolution Analytics and Gregory Piatetsky of KDNuggets will be presenting in an open-panel discussion on data science. The emphasis will be placed on extracting insights from data sets that are not big.
If you cannot wait for this webinar, Kalido has created some other very nice webinars. Just a few months ago, they hosted very good webinar titled, Data Scientist: Your Must-Have Business Investment NOW. You can watch a replay of the entire webinar.
Hal Daumé III, Assistant Professor of Computer Science at the University of Maryland, has placed the contents of his book online. The book is titled A Course in Machine Learning.
Here is a small sampling of the chapters from A Course in Machine Learning:
- Decision Trees
- Linear Models
- Neural Networks
- Ensemble Methods
- Bayesian Learning
Mohammed J. Zaki, Computer Science Professor at RPI, and Wagner Meira Jr., Computer Science Professor at Universidade Federal de Minas Gerais, have written the textbook Data Mining and Analysis: Fundamental Concepts and Algorithms. The book is currently available as a PDF download.
Based upon the chapters, the book looks very good. It contains large sections on data analysis, clustering, and classification. The final book will be published sometime in 2014.
The videos from the SciPy 2013 conference are all available online.
See all the videos at the Lanyard SciPy 2013 Conference Directory.
Here are a few of my other favorite videos:
Beyond Alphabet Soup: 5 Guidelines For Data Sharing is an excellent post by Markets for Good and Palantir Technologies. Below are the 5 Guidelines.
- Release structured raw data others can use
- Make your data machine-readable
- Make your data human-readable
- Use an open-data format
- Release responsibly and plan ahead
See the full blog post with detailed descriptions here: Beyond Alphabet Soup: 5 Guidelines For Data Sharing
I sometimes tell people I grew up dreaming of being the next Jerry Rice. My football career did not turn out as successful as his, but I was thrilled to find him in the middle of a new trend between big data and football.
As the new NFL and college football seasons begin, there is a bunch of talk about how the massive amounts of available data and statistics are changing the game for players, coaches, and fans. Fans are really pushing for more real-time statistics. Coaches want more data to create better scouting reports and game planning. Players are using the data to improve their game. I really enjoyed the brief discussion in the video about how to assign a fantasy value to offensive lineman.
Inside the Huddle: Unlocking Intelligence in Fantasy Football
Here is a link to another video interview with Jerry Rice and the Wall Street Journal discussing big data and football.