Added by Matthew Gierc on October 4, 2018 at 7:00am — No Comments
If you’ve been studying artificial intelligence and its growth, you’ll know that the industry is well past its nascent stage now. There is significant maturity in its growth, and companies from diverse backgrounds are realizing the impact of incorporating data and AI into their ecosystems.
In a bid to understand the dynamics of this data-centered…Continue
Added by Ronald van Loon on October 3, 2018 at 10:15pm — No Comments
Why Use Framework for Deep Learning?
You can implement your own deep learning algorithms from scratch using Python or any other programming language. When you start implementing more complex models such as Convolutional Neural Network (CNN) or Recurring Neural Network (RNN) then you will realize that it is not practical to implement very large models from scratch. …Continue
Added by Muhammad Rizwan on October 3, 2018 at 8:00pm — No Comments
Organizations looking for justification to move beyond legacy reporting, should review this little ditty from the healthcare industry:
The report identified six major areas of waste: unnecessary services ($210 billion annually); inefficient…Continue
Added by Bill Schmarzo on October 2, 2018 at 2:30pm — No Comments
Data Science is a broad discipline, even though the concept is recent, every day is evolving. According to Berkeley School of Information, the Data Science Life Cycle has five stages, this stages are not exclusive from one another. These five stages are Data Capture, Data Maintain, Data Process, Data Analysis and Data Communication. The latter is the most important activity in businesses. It is where we deliver Data Visualizations, Data Reports, Business Intelligence and Decision Making.…Continue
Added by Mauricio Mani on October 2, 2018 at 2:00pm — No Comments
Summary: There are several approaches to reducing the cost of training data for AI, one of which is to get it for free. Here are some excellent sources.
Added by William Vorhies on October 2, 2018 at 7:23am — No Comments
Python and R are the two most commonly used languages for data science today. They are both fully open source products and completely free to use and modify as required under the GNU public license.
But which one is better? And, more importantly, which one should you learn?
Both are widely used and are standard tools in the hands of every data scientist.
The answer may surprise you – because as a professional data scientist, you should be ready to deal with…Continue
Added by jwork.ORG on October 1, 2018 at 3:30pm — No Comments
With the recent news about Facebook and Cambridge analytica, we are rightly concerned about the power and impact of algorithms to shape political debate and more generally, our lives. The…Continue
Added by ajit jaokar on October 1, 2018 at 2:00am — No Comments