In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

This article was posted by Adrian Rosebrock on Pyimagesearch. Adrian is an entrepreneur and Ph.D who has launched two successful image search engines, ID My Pill and Chic Engine. This list is by no means exhaustive, it's simply a list of libraries that he has used in his computer vision career and found particular useful at one time or another. The goal of this blog post is to introduce you to these libraries. He encourages you to read up on each them individually to determine which one will work best for you in your particular situation.

Today, deep learning is just a cool technology, but it could mean that you won't qualify for tomorrow's best-paying jobs, even if you work in IT. Artificial intelligence (AI) is already shaking things up at Google, Facebook and IBM, and it is going to affect how work is performed in all sectors. There's already been plenty of discussion about AI supplanting so-called blue-collar work, such as trucking and railroad track inspection, but it won't stop at those seemingly easier targets. For a number of reasons, white-collar jobs could be an even more inviting target for machine learning and related technologies. Many experts have highlighted the professional impact of AI and other automation technologies, but none more presciently than Richard and Daniel Susskind in their recent book "The Future of the Professions."

Most presidential campaigns spend their time and money appealing to people who vote regularly in elections. According to a Trump campaign memo obtained by FiveThirtyEight, the campaign pursued a highly unorthodox strategy of courting unlikely voters during the primaries, focusing on people who rarely participate in GOP primary elections. The campaign relied on free media, including Trump's frequent TV appearances, to turn out regular voters, according to the memo. But survey and voter data shows that Trump won the Republican nomination thanks in large part to Republicans who typically vote in general elections, not by bringing people entirely disconnected from the electoral process to the polls. As Trump heads into the general election, the campaign's thinking during the primaries, and the ad-hoc process by which it built an operation to target and reach out to voters using data, may offer clues about how it will approach voter turnout in the fall.

It is a strong indicator of today's globalized world and rapidly growing access to Internet platforms, that we have users from over 188 countries and 500 cities globally using our Text Analysis and News APIs. Our users need to be able to understand and analyze what's being said out there, about them, their products, services, or their competitors, regardless of the locality and the language used. Social media content on platforms like Twitter, Facebook and Instagram can provide unrivalled insights into customer opinion and experience to brands and organizations. A look at online review platforms such as Yelp and TripAdvisor, as well as various news outlets and blogs, reveals similar patterns regarding the variety of language used. Therefore, no matter if you are a social media analyst, or a hotel owner trying to gauge customer satisfaction, or a hedge fund analyst trying to analyze a foreign market, you need to be able to understand textual content in a multitude of languages.

It doesn't look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that's smart enough to solve humanity's biggest problems. "Our goal's very big," says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google's DeepMind, the London-based group behind the AlphaGo software that defeated a world champion at Go in a five-game series earlier this month, setting a milestone in computing. It's supposed to be just an early checkpoint in an effort Hassabis describes as the Apollo program of artificial intelligence, aimed at "solving intelligence, and then using that to solve everything else."

This interview took place at the RE•WORK Deep Learning Summit in Boston, on 12-13 May 2016. Yoshua Bengio (PhD in CS, McGill University, 1991), post-docs at M.I.T. (Michael Jordan) and AT&T Bell Labs (Yann LeCun), CS professor at Université de Montréal, Canada Research Chair in Statistical Learning Algorithms, NSERC Chair, CIFAR Fellow, member of NIPS foundation board and former program/general chair, co-created ICLR conference, authored two books and over 300 publications, the most cited being in the areas of deep learning, recurrent networks, probabilistic learning, natural language and manifold learning. He is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Get more information on the Deep Learning Book here: http://www.deeplearningbook.org

Yoshua Bengio leads one of the world's preëminent research groups developing a powerful AI technique known as deep learning. The startling capabilities that deep learning has given computers in recent years, from human-level voice recognition and image classification to basic conversational skills, have prompted warnings about the progress AI is making toward matching, or perhaps surpassing, human intelligence. Prominent figures such as Stephen Hawking and Elon Musk have even cautioned that artificial intelligence could pose an existential threat to humanity. Musk and others are investing millions of dollars in researching the potential dangers of AI, as well as possible solutions. But the direst statements sound overblown to many of the people who are actually developing the technology.

Last week, deep learning research leader Yann LeCun took part in a Quora Session, during which he answered questions from community members on a wide variety of (mostly machine/deep learning) topics. When will we see a theoretical background and mathematical foundation for deep learning? The answer turned into a very eloquent overview of three particular thoughts on why deep learning works so well. Here is a quick overview. LeCun's first point of explanation, which maps to a good reason why deep learning works so well, is as follows: One theoretical puzzle is why the type of non-convex optimization that needs to be done when training deep neural nets seems to work reliably.

Nils Lenke is the senior director of corporate research at Nuance Communications. There is an array of neural net machine learning approaches that are simply more than just "deep." In a time when neural networks are increasingly popular for advancing voice technologies and AI, it's interesting that many of the current approaches were originally developed for image or video processing. One of those methods, convolutional neural networks (CNNs), makes it easy to see why image-processing neural nets are strikingly similar to the way our brains process audio stimuli. CNNs, therefore, nicely illuminate that our audio and visual processes are connected in more ways than one.