OpenCV is a library of programming functions mainly aimed at real-time computer vision. This course will show you how machine learning is great choice to solve real-word computer vision problems and how you can use the OpenCV modules to implement the popular machine learning concepts. The video will teach you how to work with the various OpenCV modules for statistical modelling and machine learning. You will start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to implement them with the help of real-world examples. The course will also show you how you can implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C and OpenCV.

Last fall, University of Virginia computer science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. "It would see a picture of a kitchen and more often than not associate it with women, not men," he says. That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to "train" image-recognition software. Two prominent research-image collections--including one supported by Microsoft and Facebook--display a predictable gender bias in their depiction of activities such as cooking and sports.

If you are looking to build data science models that are good for production, Java has come to the rescue. This unique video provides modern solutions to solve your common and not-so-common data science-related problems. We start with solutions to help you obtain, clean, index and search data. Then you will learn a variety of techniques to analyze data. By the end of this course, you will be able to perform all advanced operations it takes to analyze the complexity of data and to perform indexing and search operations.

There have been many kinds of books, with many kinds of meanings. This one book was special because it was the first fictional story produced via artificial intelligence. It was the first book in the sense that its contents made sense. Before this book, all other attempts of letting an AI write a book had produced things that were pastiches of randomness. A couples of sentences here and there surrounded by text that made no sense.

"Human plus machine isn't the future, it's the present," Garry Kasparov said in a recent TED talk. And this "present" is transforming the world of education at a rapid pace. With children increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards. We have already witnessed the rise and impact of education technology especially through multitude of adaptive learning platforms such as Khan Academy and Coursera that allow learners to strengthen their skills and knowledge. And now virtual reality (VR) and artificial intelligence (AI) are gaining traction. A recent report by Pearson deciphers how artificial intelligence will positively transform education in the coming years.

Last fall, University of Virginia computer science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. "It would see a picture of a kitchen and more often than not associate it with women, not men," he says. That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to "train" image-recognition software. Two prominent research-image collections--including one supported by Microsoft and Facebook--display a predictable gender bias in their depiction of activities such as cooking and sports.

Imagine if doctors could determine, many years in advance, who is likely to develop dementia. Such prognostic capabilities would give patients and their families time to plan and manage treatment and care. Thanks to artificial intelligence research conducted at McGill University, this kind of predictive power could soon be available to clinicians everywhere. Scientists from the Douglas Mental Health University Institute's Translational Neuroimaging Laboratory at McGill used artificial intelligence techniques and big data to develop an algorithm capable of recognizing the signatures of dementia two years before its onset, using a single amyloid PET scan of the brain of patients at risk of developing Alzheimer's disease. Their findings appear in a new study published in the journal Neurobiology of Aging.

The main topics concerning mathematics that you should familiarize yourself with if you want to go into data science are probability, statistics, and linear algebra. As you learn more about other topics such as statistical learning (machine learning) these core mathematical foundations will serve as a base for you to continue learning from. Let's briefly describe each and give you a few resources to learn from! Probability -- is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line. For this classic topic I recommend going with a book, such as A First Course in Probability by Sheldon Ross or Probability Theory by E.T. Jaynes.

"Human plus machine isn't the future, it's the present," Garry Kasparov said in a recent TED talk. And this "present" is transforming the world of education at a rapid pace. With children increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards. We have already witnessed the rise and impact of education technology especially through multitude of adaptive learning platforms such as Khan Academy and Coursera that allow learners to strengthen their skills and knowledge. And now virtual reality (VR) and artificial intelligence (AI) are gaining traction. A recent report by Pearson deciphers how artificial intelligence will positively transform education in the coming years.

Artificial intelligence is crossing new barriers every day -- while some tech companies are working on giving it imagination, others are working on making it capable of even more complex tasks -- for instance, judging short stories. A new study by Disney's research team and researchers at the University of Massachusetts at Boston has attempted to create artificial intelligence which will be able to understand and evaluate short stories. "Our neural networks had some success in predicting the popularity of stories. You can't yet use them to pick out winners for your local writing competition, but they can be used to guide future research," stated Boyang Li, a research scientist at Disney Research. The target of the research is to create an AI-based automated evaluation of content, although it does not have a reference point of large human evaluated story databases since not many exist.