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Now researchers in Germany have developed a new tool to help grapple with enormous data sets and reveal big picture trends, such as climatic tipping points and their effects on species. The researchers created a software package based on the Python programming language that unifies complex network theory and nonlinear time series analysis - two important data analysis concepts.

A complex network is just that - a social, biological or technological network with patterns of connections that are neither regular nor purely random. Nonlinear time series analyses are often used to look at complex systems, including those that unfold in a chaotic manner. Many natural phenomena, like changing weather patterns, are nonlinear in nature—as are man-made systems, like financial markets.

Read more at: phys.org

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As Marketers, being data-driven has become part of the job. It’s part of the revolution that has been happening for the last few years.

At the center of this process, Analytics measure conversions and attribute sales. To help them, Attribution Models tie together conversions and traffic
sources by attributing credit to the latter. The consumer’s buying journey has evolved in the last few decades. Analytics struggle to understand the flow between all touch-points and fail to consider all the factors involved in a conversion. As a result, budget is invested where it shouldn’t be, because data is
flawed by current Attribution Models. Your marketing plan is not as effective as it could be. What does the future hold? What do you need to improve your marketing activities? What about Big Data and Machine Learning? Can they help?

Problem with Current Models
Analytics are pulling out an incredible amount of data. You can know who is doing what and what led them to do so. If you don’t know much about the various Attribution Models, I recommend that you read this article: Multi-Channel Attribution Modeling. Attribution Models are incredibly helpful: they help us determine the traffic sources’ role in each conversion. However, weighting conversions – as they do – is shortsighted. They don’t take into consideration the countless factors leading to a conversion.


Read more at: linkis.com

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It is hard to understate the impact open-source has had on technology. The idea’s roots go back to the dawn of the internet boom 20 years ago, a time when nobody was getting fired for buying IBM. Open source was the new kid on the block: young, irreverent, and ready to shake up long-held convictions.

In the time since open source has gone on to power devices, infrastructure, consumer electronics, government services, and more: technology written, created, and shared by thousands around the world openly. According to Black Duck Software, an estimated 78% of companies are running open source. This is certainly evidenced by a casual wander around any one of the many technology conferences in the world: the mantra is consistent…open, open, open. Today the business of open is alive and well.

Read more at: forbes.com

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On Monday, Google released TensorFlow, a new AI system that's used in everything from recognizing speech on a noisy sidewalk to finding photos of your pet dog Fluffy.

And in a rare move for Google, it's making its software open source, meaning anyone can access and edit the code.

The software passes complex data structures, or tensors, through a neural network, or artificial brain, hence the name Tensor Flow. This process is a core part of deep learning, a powerful AI tool that is used in many of Google's products.

Google says the new program is five times faster than its first-generation system, and can be run on thousands of computers or a single smartphone. Google uses it in everything from Search to Photos to Inbox. For example, it's what lets Google Translate detect foreign words on a street sign and translate them in real-time.

Read more at: businessinsider.com