Cellular Neural Networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighboring units only. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate ( 10,000 frame/s) processing unachievable by digital processors. This python library is the implementation of CNN for the application of Image Processing. Note: The library has been cited in the research published on Using Python and Julia for Efficient Implementation of Natural Computing and Complexity Related Algorithms, look for the reference #19 in the references section.

Google is pitching its artificial-intelligence software to commercial customers in a bid to catch rivals in the increasingly lucrative business of renting its computer servers to other companies. Google is making a renewed push into the cloud business, where it spent much of its 10 billion in capital expenditures last year to build new data centers and tapped Diane Greene, a high-profile Silicon Valley executive, to run the business. A core part of Google's cloud strategy is artificial intelligence. Wednesday, Google said it would start letting cloud customers tap into two software programs it has used internally to draw meaning from text and convert speech to text. The programs use so-called machine learning, a rapidly accelerating technology that enables computers to make inferences based on data they previously had analyzed.

Even a decade ago, talking to your computer was probably a sign that you'd been working too hard and could do with a lie down. Today, no such stigma applies. That's because chatbots -- the conversational agents capable of simulating intelligent conversations with human users -- have made some massive leaps forward. From changing the way kids learn in schools to picking you out the perfect meal this evening, here are the seven of the most interesting chatbots doing the rounds at the moment. From MOOCs (Massive open online courses) to the use of iPads in schools, there's no doubt that technology is changing the way that we learn.

This new map of the world is very enlightening to scientists, in that it helps them see what areas of the world are hardest hit by poverty -- and that could be key in helping us eliminate poverty worldwide. This data is helping authorities track crop conditions, deforestation and, now, where poverty can be found across the globe where data collection by governments isn't very good. This data is potentially invaluable to humanitarian organizations and policymakers, and a team of researchers from Stanford University believes that they have created a deep-learning algorithm that can spot signs of poverty just based on the satellite images alone. It can check the conditions of roads, for example, to see how good the infrastructure in a particular area. "We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment, said in a statement.

Editor's note: You may want to check out part 1 of this tutorial before proceeding. In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/). In machine learning (ML) literature, we come across the term'training' very often, let us literally look at what that means in TF. The goal in linear regression is to find W, b, such that given any feature value (x), we can find the prediction (y) by substituting W, x, b values into the model. However to find W, b that can give accurate predictions, we need to'train' the model using available data (the multiple pairs of actual feature (x), and actual outcome (y_), note the underscore).

The outcome would be chaos. How would you structure a campaign? How would the candidates know what's important to the voters, how to position themselves, what to focus on in a particular state or town? How can anyone predict election results? Without such references, how do we apply context to any conversation?

Delta Airlines experienced a major computer outage recently, one that caused canceled flights and massive delays. Many passengers were stranded in airports, and the company now has to look back and find the cause. Most of the work will involve a close look at the IT systems Delta is using and efforts to create better redundancy, but there's also the "soft" issue of dealing with passengers who need to be rebooked on a flight or at least reassured that everything is OK. That's where A.I., machine learning, and chatbots could help, says Dave O'Flanagan, the CEO of Boxever. His company, based in Dublin, with offices in the U.S. and elsewhere, makes a cloud platform that uses A.I.-assisted analytics and machine learning.

From Fritz Lang's 1927 sci-fi epic Metropolis, to more recent incarnations in films like The Terminator and The Matrix, science fiction has always been fascinated by artificial intelligence (AI). But in our everyday lives, AI has often seemed more futuristic than realistic. As machine learning technology improves, artificial intelligence is moving out of the realm of science fiction and becoming decidedly mainstream. Nowhere is this more apparent than in marketing, where AI is becoming a virtually indispensable tool. Here are 10 examples of AI that today's marketers couldn't do without.

Press coverage and Securities and Exchange Commission scrutiny of non-GAAP measures have increased significantly over the past few months. The intense focus on these measures results from their increased use and prominence, the nature of the adjustments and the progressively large difference between the amounts reported for GAAP and non-GAAP measures. Learn what issues organizations may want to consider before preparing earnings releases and periodic filings that contain non-GAAP measures.

You may have seen the lectures before, but nonetheless, they are great to refresh your understanding on these complex topics. Grab some popcorn and enjoy!