Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Machine Learning is a great way to use your existing data to identify trends going forward. In this webinar, Gregory Deckler will show us step-by-step how to get started using this very exciting Azure Service, using Power BI and R script. Based on your requests, this webinar will show you how to bring together Azure ML, Power BI, and R in a single solution! Power BI MVP Gregory Deckler will demonstrate how to use R to bridge the gap between Azure ML and Power BI in order to build predictive analytics directly into your data model and reports. Greg is a Director at Fusion Alliance and the Solution Director of Cloud Services.

Leveraging data to predict customer satisfaction is more important than ever––it can help your business engage with customers proactively, improve operations, reduce customer churn, and improve customer relationships over the long-term. Think better support experiences for everyone. Join us for a unique opportunity to learn from guest speaker Ken Landoline, Principal Analyst at Ovum, a market-leading research and consulting business. Adrian McDermott, Senior Vice President of Product Development at Zendesk, will join Landoline to discuss how your business can use machine learning to provide a better customer experience. This webinar is complimentary, so feel free to spread the word and share with colleagues who you think might benefit from this live, interactive session.

Nomis Solutions today announced that Frank Rohde, the President and CEO of Nomis will be participating in a panel discussion entitled, "Artificial Intelligence (AI) vs. Humans in Banking" at Banking Disrupted, a leadership summit to be held at the Silicon Valley Elks Lodge in Palo Alto, CA. on Sept. 14 -15, 2016. The panel discussion will begin at 2:45 pm on Sept. 14, 2016 and include executives and user experience professionals from leading technology companies including GainX, ecosystem.AI and WiseBanyan. Algorithms and apps could replace half of banking jobs over the next 10 years, according to some forecasts. At the same time, people still prefer person-to-person interactions when it comes to big financial decisions. The panel will address some key questions including: how AI will change banking, and if the human touch will still be necessary in the future.

This tutorial will explain some practical tips about how to train a neural machine translation system. It is partly based around examples using the lamtram toolkit. Note that this will not cover the theory behind NMT in detail, nor is it a survey meant to cover all the work on neural MT, but it will show you how to use lamtram, and also demonstrate some things that you have to do in order to make a system that actually works well (focusing on ones that are implemented in my toolkit). This tutorial will assume that you have already installed lamtram (and the cnn backend library that it depends on) on Linux or Mac. Then, use git to pull this tutorial and the corresponding data. The data in the data/ directory is Japanese-English data that I have prepared doing some language-specific preprocessing (tokenization, lowercasing, etc.). Machine translation is a method for translating from a source sequence F with words f_1, ..., f_J to a target sequence E with words e_1, ..., e_I. This usually means that we translate between a sentence in a source language (e.g.

It's a struggle because deep learning is taught by academics, for academics. The way practitioners learn new technologies is by developing prototypes that deliver value quickly. This is a top-down approach to learning, but it is not the way that deep learning is taught. A way that works for top-down practitioners like you. You will believe that being successful with applied deep learning is possible.

What does "causality" mean, and how can you represent it mathematically? How can you encode causal assumptions, and what bearing do they have on data analysis? These types of questions are at the core of the practice of data science, but deep knowledge about them is surprisingly uncommon. If you analyze data without regard to causality, you open your results up for the possibility of enormous biases. This includes everything from recommendation system results, to post-hoc reports on observational data, to experiments run without proper holdout groups. I've been blogging a lot recently about causality, and wanted to go through some of the material at a more technical level. Recent posts have been aimed at a more general audience. This one will be aimed at practitioners, and will assume a basic working knowledge of math and data analysis. To get the most from this post you should have a reasonable understanding of linear regression and probability (although we'll review a lot of probability). Prior knowledge of graphical models will make some concepts more familiar, but is not required. Judea Pearl, in his book Causality, constantly remarks that until very recently, causality was a concept in search of a language.

IBM (NYSE: IBM) today announced THINK Marketing, a new one-stop destination for marketers to gain knowledge, learn skills and ultimately drive a digital transformation within their business. Designed to help Chief Marketing Officers (CMO) and their teams build proficiency and experience, THINK Marketing delivers news and thought leadership content from the industry's top marketing influencers and news outlets. Through these assets, marketers can gain a deeper knowledge and, when ready, match their needs with solutions from IBM and more than sixty marketing technology companies from around the globe including Sprinklr, Mirakl and MediaMath. THINK Marketing will rapidly grow to include new industry specific content, collections of content on additional marketing functions and double the number of third-party solutions. THINK Marketing will also include a developer marketplace where start-ups and developers can virtually brainstorm, try solutions and also create and bring to market new cognitive and cloud apps that address important marketing challenges.

UserReplay announced today the addition of a machine learning feature to its existing solution in order to better assist companies with gaining insight into their customers' online experiences and resolve issues in real time. UserReplay machine learning uncovers hard-to-discover revenue opportunities hidden in the powerful data set captured by UserReplay. UserReplay's machine learning algorithm uncovered the issue and the high-fidelity replay showed the retailer how to fix the problem. About UserReplay UserReplay's customer experience analytics solution enables businesses to discover the truth about their customers' digital experience.