Saffron Technology has been on a quest since 1999 to replicate the way the human brain learns using associative memory. Saffron is now commercially available as a cognitive computing platform following beta testing for real-time operational risk intelligence and decision support in defense, energy, healthcare and manufacturing applications.
It was Saturday, June 14 and I was up at the crack of dawn (which is quite an achievement for a late night data hacker like me) to get over to the Big Data Camp 2014 happening at the DirectTV campus in beautiful El Segundo, Calif. (actually a spartan industrial area just south of LAX). I was anticipating a great day of big data technology focusing on three advertised session tracks: Data Science, Hadoop, and NoSQL.
Deep learning is a new force in the field of data science – a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations. The talk below breaks the ice for those not familiar with this technology. This talk was presented at the SF Neural Network Aficionados Discussion Group hosted by NextSpace in San Francisco.
I love a good data science competition to let me stretch my arms around a good problem. Kaggle is one of my favorite destinations these days to learn about all the innovative ways machine learning is being applied to real-life business problems. So I was pleasantly surprised to see this new challenge sponsored by Algomost, an international data mining platform.
Ersatz Labs, the company behind the deep learning platform Ersatz™, has announced the out-of-beta launch of its flagship product. Ersatz is the first deep learning platform and is available either as a cloud service or as a deep learning “appliance”, a combination of neural network hardware and software geared toward large enterprises.
Everyone knows that data scientists love data and the more of it, the greater the love. As a result, the surging interest in wearables is just what the doctor ordered because these electronic devices collect enormous treasure troves of data. In turn, it is the job of data scientists to make sense of it all, unlock secrets, and assign economic value. As a data scientist, it is a dream come true!
A very hot topic in data science these days is the ability to discern “sentiment” by analyzing text from social media sources. In the talk below, Ryan Rosario presents some of his work at Facebook – “Sentiment Classification Using scikit-learn.” Ryan is a Quantitative Engineer at Facebook.
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