Yahoo has announced TensorFlowOnSpark, its latest open source framework for distributed deep learning on big data clusters. Deep learning (DL) has evolved significantly in recent years. At Yahoo, we've found that in order to gain insight from massive amounts of data, we need to deploy distributed deep learning. Existing DL frameworks often require us to set up separate clusters for deep learning, forcing us to create multiple programs for a machine learning pipeline (see Figure 1 below). Having separate clusters requires us to transfer large datasets between them, introducing unwanted system complexity and end-to-end learning latency.

Machine learning encompasses an important group of algorithms and technologies that are becoming ever more ubiquitous in our jobs and in our daily lives. H2o.ai is a powerful, open-source tool for doing machine learning. This talk will attempt to answer some important questions around machine learning like, what is it exactly? And why is it so popular right now? This talk will also lay out some very basic machine learning theory, give some practical advice for applied practitioners, and provide an introduction on how h2o works as a technology.

Will artificial intelligence get more aggressive and selfish the more intelligent it becomes? A new report out of Google's DeepMind AI division suggests this is possible based on the outcome of millions of video game sessions it monitored. The results of the two games indicate that as artificial intelligence becomes more complex, it is more likely to take extreme measures to ensure victory, including sabotage and greed. The first game, Gathering, is a simple one that involves gathering digital fruit. Two DeepMind AI agents were pitted against each other after being trained in the ways of deep reinforcement learning.

The most vexing point when it comes to autonomous vehicle technology is how it'll perform in the rain or the snow, which serve as one of the environmental scenarios that can stymie how a robot car performs. That's why a new video from AV system startup Drive.ai The nearly four-minute clip is one of the few instances that has captured an self-driving car moving about in inclement weather, without a driver intervening. "Any successful self-driving technology will need to address countless unpredictable situations and a wide range of driving conditions, yet few are able to today," Drive.ai, Drive.ai said it uses "deep learning technology" to develop its AV system, which it says allows the technology to develop and learn like a human brain would.

In the past few years, the terms machine learning (ML) and deep learning have begun showing up frequently in many technology news and websites. The major difference between machine learning and other statistical methods, like data mining, is a popular subject of debate. In laymen's language, ML and data mining process use many of the same algorithms and techniques but one major difference lies in what the two methods predict. While data mining is used to uncover previously unknown patterns and knowledge, Machine learning is used to reproduce known patterns and knowledge. ML provides algorithms that resolve the problem based on the data, and the solution improves with time.

The dataset consists of about 12,000 headlines half of which are clickbait. The clickbait headlines were fetched from BuzzFeed, NewsWeek, The Times of India and, The Huffington Post. The genuine/non-clickbait headlines were fetched from The Hindu, The Guardian, The Economist, TechCrunch, The wall street journal, National Geographic and, The Indian Express. Some of the data was from peterldowns's clickbait-classifier repository I used Stanford's Glove Pretrained Embeddings PCA-ed to 30 dimensions. This sped up the training.

When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on health care. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with AI we could achieve exponential breakthroughs. Deep learning first caught the media's attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Recently, a multidisciplinary research team at Stanford's School of Medicine comprised of pathologists, biomedical engineers, geneticists, and computer scientists developed deep learning algorithms that diagnose lung cancer more accurately than human pathologists. The ultimate dream in health care is to eradicate disease entirely.

Expect new Facebook apps on your TVs, home security cameras that just know where your doors are, and the return of the Nokia 3310 -- for some reason. You'll have to subscribe for Nest's latest features.Nest cams can detect your doors automatically Over the next few weeks, Nest Aware customers will see automatic door detection appear on both their indoor and outdoor Nest Cam feeds. The cameras will attempt to recognize motion patterns over time, feeding the data into deep learning algorithms to make it all automated, creating "activity zones" around the doors it picks up. 'Gran Turismo' passed $1 billion in 2013'Forza' franchise tops $1 billion in sales Microsoft's flagship racing nameplate has yet to displace Gran Turismo from the throne, but thanks to Forza' popularity and consistency, it's officially the "best-selling racing franchise of this console generation." With nine titles to its credit, Forza has transitioned to a well-received annual release schedule, while GT's last full-fledged game was Gran Turismo 6 in 2013 for the PS3.

A new startup debuted software that it claims will disrupt the emerging field of machine learning. Gamalon uses complex math and algorithms instead of the high-end processors required for the deep-learning techniques becoming popular with the likes of Facebook and Google. "This is a big breakthrough and will be a sea change in artificial intelligence," said Ben Vigoda, who sold Lyric Semiconductor to Analog Devices in 2011. The startup came out of stealth mode the same day a rival, GraphCore, revealed some details about the software behind its AI processor which is tailored to the current deep-learning techniques (see page 3 for more). Today's neural network approaches require training with the huge data sets typically only web giants possess. The job takes so much processing power that Google created its own accelerator, Facebook designed an open-source GPU server, and Intel acquired two chip startups to handle it.

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've 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 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.