Originally posted on Google Research blog It has been an eventful year since the Google Brain Team open-sourced TensorFlow to accelerate machine learning research and make technology work better for everyone. There has been an amazing amount of activity around the project: more than 480 people have contributed directly to TensorFlow, including Googlers, external researchers, independent programmers, students, and senior developers at other large companies. TensorFlow is now the most popular machine learning project on GitHub. With more than 10,000 commits in just twelve months, we've made numerous performance improvements, added support for distributed training, brought TensorFlow to iOS and Raspberry Pi, and integrated TensorFlow with widely-used big data infrastructure. We've also made TensorFlow accessible from Go, Rust, and Haskell, released state-of-the-art image classification models – and answered thousands of questions on GitHub, StackOverflow, and the TensorFlow mailing list along the way.

Data Analytics is one of the most sought-after skill sets today, with students and professionals alike aspiring to be enabled with the necessary skills to derive data-driven business insights in their careers. It also helps organisations attain a competitive advantage over others. Data Analytics is not limited to mathematicians, statisticians or IT professionals with programming skills. The need to analyse data has become so elementary today that a professional in any business is expected to know the necessary skills. While professionals today are aware of the need to be trained, some are unaware of how to embark on a career in analytics.

This repository contains the TSFRESH python package. "Time Series Feature extraction based on scalable hypothesis tests". The package contains many feature extraction methods and a robust feature selection algorithm. Data Scientists often spend most of their time either cleaning data or building features. While we cannot change the first thing, the second can be automated.

It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder. Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don't want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise.

It seems only yesterday that we were lying on our friend's sofa vowing to abstain from all alcohol for the entirety of 2016 before being coerced into a pub trip by our pesky co-workers the first Friday back in the office. Yes, the start of the year seems no time ago at all, but 2016 is nearly over. There are under 9 weeks left of the year and this, coupled with the arrival of the colder weather, has got us feeling all nostalgic. Let's recap some of the biggest tech innovations of 2016 and our predictions for the tech world in 2017. Well Pokémon Go launched in July and thanks to the enormous number of nostalgic noughties kids roaming the streets with little to do, it took off at an astonishing rate.

Artificial intelligence is one of the most buzzed-about terms in technology. The AI market is estimated to reach $5.05 billion USD by 2020, up from $419.7 million USD in 2014 – a 53% increase. With the launch of Facebook's chatbots, Amazon's Echo, and IBM's Watson, companies in many fields are considering how they can use new AI tools to their advantage. Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes. An everyday example is Facebook's targeted ads, which use artificial intelligence to narrow target segments down in a matter of hours.

Science fiction has, for many years, looked to a future in which robots are intelligent and cyborgs are commonplace. The Terminator, The Matrix, Blade Runner and I, Robot are all good examples of this vision. But until the last decade, consideration of what this might actually mean in the future was unnecessary because it was all science fiction, not scientific reality. Now, however, science has not only done some catching up; it's also introduced practicalities that the original story lines didn't appear to include (and, in some cases, still don't include). What we consider here are several different experiments linking biology and technology together in a cybernetic way--essentially ultimately combining humans and machines in a relatively permanent merger. When we typically first think of a robot, we regard it simply as a machine.

AI experts build'neural network' to help researchers search for dugongs Dugong expert Dr Amanda Hodgson estimates she has stared at more than 30,000 photographs of blue water. "It's really taxing on your eyes and it's hard to maintain concentration." The researcher from WA's Murdoch University has been scanning pictures captured by aerial drones in a search for dugongs, to work out their population, size and location. Globally dugongs are classed as "vulnerable to extinction" and are found in waters off the northern half of Australia. "There are areas where they're quite vulnerable because their habitat overlaps with coastal development."

AI experts build'neural network' to help researchers search for dugongs If You're New To Machine Learning, Be Choosy About What You Read Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

We just came back from a week at an idyllic beach resort in Malaysia. Not having to wear shoes for a week felt liberating. Internet was non-existent in most parts of the island and sketchy at best at upmarket resorts. Even though it was utopia for a week, we could not wait to get back on the grid. Technology is a double-edged sword.