MOOC Video Collection 777 views Decision Theory: Utility Functions - Stanford University - Duration: 18:16.

Machine learning- to put it mildly- is an incredibly broad and varied field, with multitudes of applications. Thus, writing a list entitled "10 Machine Learning Experts You Need to Know" proves challenging for a number of reasons. Firstly, I've restricted my ten picks to those currently working in the field- if I extended it to those living and passed, I never would have been able to identify only ten worthy of mention. Secondly, this list is in no way ranked- how would I decide which is more remarkable? Third, this is by no means an exhaustive list of people currently making significant contributions to the field of machine learning, or the wider world.

So many of the cartographers I've gotten to know while writing about maps seem to genuinely love their jobs. It's one of those professions with a disproportionate number of people who are really happy to be there. I suspect that one reason for this could be that many of them have loved maps since they were kids, and they've managed to turn that love into a career. This collection of childhood maps made by eight professional cartographers backs up that theory. I interviewed each of them about their early mapmaking, how they found their way into cartography, and what they love about their jobs today. Their stories all have their individual quirks, but there are some common threads.

Summary: Looking beyond today's commercial applications of AI, where and how far will we progress toward an Artificial Intelligence with truly human-like reasoning and capability? This is about the pursuit of Artificial General Intelligence (AGI). There is no question that we're making a lot of progress in artificial intelligence (AI). So much so that we are rapidly approaching or have already arrived at a plateau in development where more effort is being put into commercializing existing AI capabilities than in improving it. As far back as November 2014 Kevin Kelly, cofounder of Wired magazine and prolific futurist observed "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Well Kevin, you're right.

A recent survey found that 46% of Americans can't come up with $400 to cover an unexpected expense without selling an asset or taking out a loan. Many Americans are in debt, living paycheck to paycheck, or struggling to save for retirement -- or all three. This is especially true for Millennials, who are carrying the heaviest student loan debt burden in US history. Now more than ever, Americans are in dire need of personal financial management (PFM) tools that empower them to take control of their finances and grow their wealth. But most banks aren't meeting this need.

Whether it's ads that predict our buying behaviors or sophisticated image searching, machine learning is here and in our technology. Get into the new wave with the Deep Learning and Artificial Intelligence Introductory Bundle. This bundle dives into the powerful algorithms that produce our most sophisticated technology. More and more companies are relying on the concepts of deep learning and machine learning to produce machine responses that evolve and adapt to human actions -- just think of your Netflix recommendations or suggested contacts on Facebook. With four courses on Python, data science and more, you'll set yourself apart from the pack with a deeper understanding of the latest revolution sweeping current technology.

The way we learn natural languages hasn't really changed for decades. We now have beautiful apps like Duolingo and Spaced Repetition software like Anki, but I'm talking about our fundamental approach. We still follow pre-defined curricula, and do essentially random exercises. Learning isn't personalized, and learning isn't driven by data. And I think there's a big opportunity to change that.

This screencasts describes how to import a Neural Network that was created and trained using Keras, into DeepLearning4J Deeplearning4j - Skymind uploaded a video 2 weeks ago Skymind Academy - Duration: 91 seconds. Skymind Academy enables your team to build deep learning solutions. We offer private corporate seminars and public workshops. Deeplearning4j - Skymind uploaded a video 3 weeks ago What is Deep Learning? We explain what deep learning is and why it matters.

All eyes are on Montreal these days as a hub for deep learning. "Clearly it's a place where everybody wants to be if we want to tap into that talent," says Nagraj Kashyap, corporate vice-president of Microsoft Ventures in San Francisco. Montreal's pre-eminence as a deep learning centre can largely be attributed to the efforts of Yoshua Bengio, considered to be one of the three "co-fathers" of deep learning technology. Bengio not only engaged in cutting-edge research at the Université de Montréal long before deep learning was considered viable; his work has spawned an ecosystem that many say is unrivalled in the artificial intelligence (AI) world. That ecosystem includes the Montreal Institute for Learning Algorithms (MILA) which has been funded by government and private sector parties, including Google and Microsoft, among other tech notables.

Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction. However, most of the work focuses on finding distributed representations of nodes, which are inherently ill-suited to tasks such as community detection which are intuitively dependent on subgraphs. Here, we propose sub2vec, an unsupervised scalable algorithm to learn feature representations of arbitrary subgraphs. We provide means to characterize similarties between subgraphs and provide theoretical analysis of sub2vec and demonstrate that it preserves the so-called local proximity. We also highlight the usability of sub2vec by leveraging it for network mining tasks, like community detection. We show that sub2vec gets significant gains over state-of-the-art methods and node-embedding methods. In particular, sub2vec offers an approach to generate a richer vocabulary of features of subgraphs to support representation and reasoning.