A change in cultural norms influences changes in intellectual property - and corporate communications. JL Tripp Mickle reports in the Wall Street Journal: The battle for artificial-intelligence expertise is forcing Apple Inc. AAPL 1.01% to grapple with its famous penchant for secrecy, as tech companies seek to woo talent in a discipline known for its openness. "We come from a community where we share ideas and get credit for it and a lot of us would be very unhappy to give that up," The battle for artificial-intelligence expertise is forcing Apple Inc. AAPL 1.01% to grapple with its famous penchant for secrecy, as tech companies seek to woo talent in a discipline known for its openness. The technology giant this year has been trying to draw attention--but only so much--to its efforts to develop artificial intelligence, or AI, a term that generally describes software that enables computers to learn and improve functions on their own. Apple launched a public blog in July to talk about its work, for example, and has allowed its researchers to speak at several conferences on artificial intelligence, including a TED Talk in April by Tom Gruber, co-creator of Apple's Siri voice assistant, that was posted on YouTube last month.

In this day and age of digital marketing, having the right visual content can increase the brand value significantly. But not everyone is equipped to create professional graphics and videos on their own. This means people spend thousands of dollars in hiring designers or buying expensive softwares, which have deep learning curves and are not easy to use. The solution would be to use readily-available tools like PowerPoint or use free online tools which are easy to use. With Visual Deck, you can learn to create your own professional graphics, presentations and animated videos with no prior experience whatsoever!

You will engage in 5 hands-on labs for creating advanced models …. Because our goal is to get you up to speed as quickly as possible, we'll cover through the 30 lectures, The course will enable you to extract actionable insight from your customers' data According to a Mckinsey Study, demand for data scientists is projected to exceed supply by more than 50% by 2018. That's the gap you as citizen Data Scientists are going to fill Citizen data scientist are "Business people with the right attitude - curious, adventurous, determined - to research and improve things in the organization" SAS The need is so, that according to Gartner, by 2017, the number of citizen data scientists will grow 5 times faster than the number of highly skilled data scientists. This course is designed for business professionals: marketer, manager and analytical minds in every department … who want to take their skills to the next level. If you have solid business knowledge, curious, determined to improve things in your company or just willing to learn new methods and tools than this course is for you….

Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you! Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics.

The online problem of computing the top eigenvector is fundamental to machine learning. In both adversarial and stochastic settings, previous results (such as matrix multiplicative weight update, follow the regularized leader, follow the compressed leader, block power method) either achieve optimal regret but run slow, or run fast at the expense of loosing a d factor in total regret where d is the matrix dimension. We propose a follow-the-compressed-leader (FTCL) framework which achieves optimal regret without sacrificing the running time. Our idea is to "compress" the matrix strategy to dimension 3 in the adversarial setting, or dimension 1 in the stochastic setting.

If you sell your expertise for a living, you will discover significant benefits in transferring your knowledge to the world of Artificial Intelligence (AI). It is now possible to create an online chatbot with near human characteristics to deliver your intellectual property to the world. From your website your clients will be able to interact with the AI system to receive a personalised experience of the way you deliver your specialist skills. The Chatbot will be able to track the client's progress and adapt the learning experience to suit their individual mood, personality and abilities. Your unique talents will be instantly made available to a global audience 24 / 7. Up until August 2016 the software to build a chatbot has been in prototype with major corporations but now it's going mainstream.

If you are practitioner interested in deep learning for NLP, you may have different goals and requirements from the material. For example, you may want to focus on the methods and applications rather than the foundational theory. The course is comprised of 13 lectures, although the first and second lectures are both split into two parts. The complete lecture breakdown is provided below. The GitHub repository for the course provides links to slides, flash videos and reading for each lecture. I would recommend watching the videos via this unofficial YouTube playlist. Below is a course overview slide taken from the first lecture.

Every face does not tell a story; it tells thousands of them. Over evolutionary time, the human brain has become an exceptional reader of the human face--computerlike, we like to think. A viewer instinctively knows the difference between a real smile and a fake one. In July, a Canadian study reported that college students can reliably tell if people are richer or poorer than average simply by looking at their expressionless faces. Scotland Yard employs a team of "super-recognizers" who can, from a pixelated photo, identify a suspect they may have seen briefly years earlier or come across in a mug shot.

When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. We'll show how you can evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall, and present some solutions that can help when you encounter such scenarios. If we were to train a machine learning model and it learned to always predict an email as not spam (negative class), then it would be accurate 99% of the time despite never catching the positive class. Similarly, increasing the number of training examples can help in cases of high variance, helping the machine learning algorithm build a more generalizable model.

So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. If you already have some background, or don't need all the components, feel free to adapt your own paths and let us know how you made changes in the path.