Any gradient descent optimization requires to choose a learning rate. With deeper and deeper models, tuning that learning rate can easily become tedious and does not necessarily lead to an ideal convergence. We propose a variation of the gradient descent algorithm in the which the learning rate is not fixed. Instead, we learn the learning rate itself, either by another gradient descent (first-order method), or by Newton's method (second-order). This way, gradient descent for any machine learning algorithm can be optimized.

Last Thanksgiving, I watched my father-in-law evaluate over one hundred exams for the high school class he teaches on the U.S. government. They were mostly short answer questions: matching different provisions of the U.S. Constitution, and explaining the contents of the Bill of Rights. The grading was tedious and time consuming, and took him hour after hour during what should have been a holiday. I started to wonder whether there could be a faster way. Automatic computer grading could do exactly that, learning from previous answers and getting better as it goes -- and it is already being used in some universities and for large online courses (MOOCs).

Executives increasingly see investing in retraining and "upskilling" existing workers as an urgent business priority that companies, not governments, must lead on. The world of work faces an epochal transition. By 2030, according to the a recent McKinsey Global Institute report, Jobs lost, jobs gained: Workforce transitions in a time of automation, as many as 375 million workers--or roughly 14 percent of the global workforce--may need to switch occupational categories as digitization, automation, and advances in artificial intelligence disrupt the world of work. The kinds of skills companies require will shift, with profound implications for the career paths individuals will need to pursue. How big is that challenge?

Candidates hoping to land their dream job are increasingly being asked to play video games, with companies like Siemens, E.ON and Walmart filtering out hundreds of applicants before the interview stage based partly on how they perform. Played on either smartphones or computers, the games' designers say they can help improve workplace diversity, but there are questions over how informative the results really are. To the casual observer, many of the games might seem almost nonsensical. One series of tests by UK-based software house Arctic Shores includes a trial where the player must tap a button frantically to inflate balloons for a party without bursting them. In another, the candidate taps a logo matching the one displayed on screen, at an ever more blistering pace.

Booz Allen Hamilton has been at the forefront of strategy and technology for more than 100 years. Today, the firm provides management and technology consulting and engineering services to leading Fortune 500 corporations, governments, and not-for-profits across the globe. Booz Allen partners with public and private sector clients to solve their most difficult challenges through a combination of consulting, analytics, mission operations, technology, systems delivery, cybersecurity, engineering and innovation expertise. So you want to be a Data Scientist? Booz Allen Hamilton is no longer just a consulting firm.

Of course it is, as mostly that's the actual science part in data science. But it doesn't mean that you couldn't learn it by yourself if you are smart and determined enough. In this article, I am going to list 6 books that I recommend to start with to learn statistics. The first three are lighter reads. These books are really good for setting your mind to think more numerical, mathematical and statistical.

In 1978 a young woman named Maria Klawe arrived at the University of Toronto to pursue a doctorate in computer science. She had never used a computer--much less written a line of code--but she had a PhD in math and a drive to succeed in a male-dominated field. She was so good that, nine months later, the university asked her to be a professor. Today, however, computer science is one of the few STEM fields in which the number of women has been steadily decreasing since the '80s. In the tech industry, women hold only around one-fifth of technical roles.

PREDICTIVE DATA: Degree Analytics, a startup that aims to use data and AI to increase college students' graduation rates, has raised $1 million in a "Seed B" round. The Austin-based company's investors include Rick Dalzell, formerly CIO at Amazon; David Palumbo, managing director at SoftMatch; and Larry Benz, founder of physical therapy group Confluent Health. Degree Analytics gathers student information such as attendance, library time or LMS login activity, and uses that data to make predictions on students' future behaviors. It will flag school counselors or instructors if they should intervene with a student. The company claims its platform is currently being used at three institutions, including Eastern University, and that the latest funding round will go towards further developing its platform and expanding both its "domestic and international presence."

Still founder-led by Chairman and Chief Executive Officer Richard Fairbank, Capital One is on a mission to help our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We measure our efforts by the success our customers enjoy and the advocacy they exhibit. We are succeeding because they are succeeding. Guided by our shared values, we thrive in an environment where collaboration and openness are valued. We believe that innovation is powered by perspective and that teamwork and respect for each other lead to superior results.

MACHINE LEARNING FOR APPS Welcome to the most comprehensive course on Core ML, one of Apples hot new features for iOS 11. The goal with Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech. Dive in and learn the core concepts of machine learning and start building apps that can think! In this course you going to learn everything you need to know to start building more intelligent apps and your own ML Models.