Goal of the meetup is to dive into the Deep learning space. To start off with we will be going through the lectures of a Deep learning course on Udacity and working on the assignments (of course, we will maintain the "honor of code"). Once we are done with that we will take off with reading popular deep learning papers and implementing them. Currently this meetup is mostly for people who have some knowledge of machine learning but not deep learning. If you are an expert in deep learning then you are most welcome to join but we may not have much to offer, unless you want to brush up your DL skills or are interested in guiding DL enthusiasts.

All bets are off on how quickly learning occurs, how fast it's implemented via autonomous enterprises, and the extent to which machines drive the global economy. The digital AI future is here, and it's going to radically change everything about the way we live and work. If that sounds far-fetched, just follow the money: From 2011 to 2015, investments in AI startups skyrocketed from $282 million to $2.4 billion. Advances in big data technologies combined with inexpensive, massively scalable infrastructure and storage solutions are opening up new applications for AI. The technology helps us perform tasks better and infinitely faster.

Don't know where or how to start learning? But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.

Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. "We are in the midst of an artificial intelligence security revolution," says Dimitrios Pavlakis, Industry Analyst at ABI Research. "This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years."

Morever, these algorithms are robust, so don't require problem-specific hand-tuning. One powerful example is sampling from an arbitrary probability distribution, which we need to do often (and efficiently!) when doing inference. The brute force approach, rejection sampling, is problematic because acceptance rates are low: as only a tiny fraction of attempts generate successful samples, the algorithms are slow and inefficient. See this post by Jeremey Kun for further details. Until recently, the main alternative to this naive approach was Markov Chain Monte Carlo sampling (of which Metropolis Hastings and Gibbs sampling are well-known examples). If you used Bayesian inference in the 90s or early 2000s, you may remember BUGS (and WinBUGS) or JAGS, which used these methods. These remain popular teaching tools (see e.g.

SAN FRANCISCO: A charitable foundation backed by Mark Zuckerberg and his wife has said it has bought a Canadian artificial intelligence startup as part of a mission to eradicate disease. The Chan Zuckerberg Initiative did not disclose financial terms of the deal to acquire Toronto-based Meta, which uses AI to quickly read and comprehend scientific papers and then provide insights to researchers. Meta capabilities will be unified in a tool made available for free to scientists. "We are very excited about what lies ahead," Meta cofounder and CEO Sam Molyneux said in a statement. Zuckerberg and his doctor wife, Priscilla Chan, in September pledged $3 billion over the next decade to help banish or manage all disease, pouring some of the Facebook founder's fortune into innovative research.

When the California Department of Motor Vehicles released its annual autonomous vehicle disengagement report Wednesday, Google's self-driving platform, Waymo, came away looking like the big moves it made this year were well worth the fuss. The company posted significant year-over-year improvements in its self-driving tech's safety and efficiency, even as it ramps up its autonomous efforts. As its name implies, the DMV report tracks the total number of disengagements that occur while the self-driving cars log test miles on the state's roads. Disengagements are instances when a driver takes manual control of a test vehicle in autonomous mode to correct its trajectory. According to the report, which was first noted by The Verge, Waymo's test cars logged 635,868 miles in 2016 and experienced 124 "reportable disengagements."

For people living with Asperger's syndrome, every social interaction can be a battle. While high-functioning in some aspects, those suffering from the form of autism often struggle to engage with other people and topics outside of their own spheres of interest. Keeping up with conversations can be especially challenging then, since difficulty interpreting the meaning of nonverbal communication (like gestures and facial expressions) and modulations in the speech patterns of others is one of the hallmarks of the condition. A pair of MIT researchers have set out to make these interactions less harrowing. Using wearable tech and AI deep-learning systems, they've developed a tool that could someday act as a real-time virtual social coach.

For energy generation, utilities, and oil and gas, IoT security means predictive maintenance, cyber defense, and threat remediation. In this white paper, leading Artificial Intelligence company, SparkCognition, details use cases with Fortune 500 clients addressing the gap between the vast amount of data being collected and the limited resources to analyze this important information.

The idea was to greatly simplify SEO for most publishers and to remind them that the finer points of SEO don't matter if you don't get the basics right. The reason that machine learning is important to this picture is that search engines are investing heavily in improving their understanding of language. Hummingbird was the first algorithm publicly announced by Google that focused largely on addressing an understanding of natural language, and RankBrain was the next such algorithm. We also know that Google (and other engines) are interested in leveraging user satisfaction/user engagement data as well. Though it's less clear exactly what signals they will key in on, it seems likely that this is another place for machine learning to play a role.