Summary: The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and lower skilled data scientists. Salary increases show us whether demand is increasing, and finally we take a shot at determining which skills are most in demand.
Added by William Vorhies on July 1, 2019 at 8:00am — No Comments
Integrating Pivot Billions with Keras Deep Learning to enhance currency trading models with AI to achieve over 30% net profit in less than 7 months.
Deep Learning has revolutionized the fields of image…
Added by Benjamin Waxer on February 8, 2019 at 5:29am — No Comments
Added by Kyrylo Kolodiazhnyi on January 21, 2019 at 7:44am — No Comments
While a reliable method to accurately identify suicidal patients is missing from medical literature, researchers…Continue
Added by Deena Zaidi on August 15, 2018 at 6:42pm — No Comments
Added by Yoel Zeldes on August 15, 2018 at 10:30am — No Comments
As deep neural networks (DNN) become more powerful, their complexity…Continue
Added by Yoel Zeldes on August 11, 2018 at 9:00am — No Comments
Summary: Before starting to develop an AI strategy, make sure your team understands the limits of what is reasonable today, as well as incremental improvements that might be overlooked. Focus should be on your LOB leaders who understand the business. Make sure they are also able to recognize AI opportunities.
Added by William Vorhies on May 8, 2018 at 9:30am — No Comments
Summary: Deep Learning, based on deep neural nets is launching a thousand ventures but leaving tens of thousands behind. Transfer Learning (TL), a method of reusing previously trained deep neural nets promises to make these applications available to everyone, even those with very little labeled data.
Added by William Vorhies on April 17, 2018 at 12:25pm — No Comments
I’d like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In…Continue
Summary: GDPR carries many new data and privacy requirements including a “right to explanation”. On the surface this appears to be similar to US rules for regulated industries. We examine why this is actually a penalty and not a benefit for the individual and offer some insight into the actual wording of the GDPR regulation which also offers some relief.
Summary: Some observations about new major trends and directions in data science drawn from the Strata Data conference in San Jose last week.
Summary: Here are our 6 predictions for data science, machine learning, and AI for 2018. Some are fast track and potentially disruptive, some take the hype off over blown claims and set realistic expectations for the coming year.
Summary: As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. “Machine Learning” is just the most recent case in point. It’s had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term. Come on folks. Let’s make up our minds.
As a profession we do a pretty poor job of agreeing on good naming conventions…Continue
Summary: Which is more important, the data or the algorithms? This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years. And it all leads back to Hadoop.
Summary: The addition of AI capabilities to our personal devices, applications, and even self-driving cars has caused us to take a much deeper look at what we call ‘User Experience’ (Ux). A more analytical framework identified as Cognitive Ergonomics is becoming an important field for data scientists to understand and implement.
Added by William Vorhies on October 31, 2017 at 9:51am — No Comments
Artificial intelligence or AI for short is the field of making computer think like humans by creating an artificial brain. Whatever the human can do intelligently is required to be moved into machines. The machine will just do what the human tells it and no more. For example, the human can sort numbers in an intelligent manner and so machines should be intelligent by sorting numbers like humans. To do this, there are a number of algorithms like bubble sort that allows the machine to think…Continue
Summary: In just the last 10 months based only on facial characteristics deep learning has been used to predict who is a criminal and who is gay. These are rigorous, peer reviewed studies published in academic journals. How should this knowledge be used and how will the public react?
Summary: Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models. Here are a few things you need to know.
Added by William Vorhies on June 13, 2017 at 8:00am — No Comments
Summary: Quantum computing is now a commercial reality. Here’s the story of the companies that are currently using it in operations and how this will soon disrupt artificial intelligence and deep learning.
Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.
Deep Learning is the modern buzzword for artificial neural networks, one of many concepts…Continue