In an age where everything is changing –and changing fast– it's easy to forget how much we've progressed. While we may not have floating cars or robotic teachers, we are on the brink of some very exciting and dramatic developments across all industries. As one of the principal drivers of progression, it's no surprise that learning –and education in general– has been a focus of technological advances. While eLearning is not a new concept, its popularity is increasing, especially as technology becomes more affordable. A big barrier for eLearning is the cost of developing content.

Matthew Peterson is a pretty inspirational guy. As a dyslexic child he found math class difficult, so as an adult he resolved to totally change the way math is taught. After completing his studies in biology, electrical engineering, and Chinese language and literature at the University of California, Irvine, Peterson co-founded the nonprofit MIND Research Institute and set about developing "Spatial Temporal (ST) Math," a computer game-based method of teaching that doesn't rely on language as a medium. Instead it uses spatial-temporal reasoning--the ability to move stuff around in your mind and work out how it fits together. Proponents point to recent findings in neuroscience and education research--showing that early music training can enhance spatial-temporal reasoning, for example--as justification for this shift.

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data.

In his two seasons with the Ravens, John Urschel has started at three positions -- left guard, center, and right guard. Now, he's back in school trying to earn a third degree. As a collegiate standout at Penn State, Urschel earned his bachelor's degree and master's degree in math there, and was working on a second master's in math education when he was a fifth-year senior. That meant teaching classes on top of his football workload. But the balance was never an issue for Urschel, one he said in his first minicamp with the Ravens was one he enjoyed.

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty Take some time to learn about the human brain! This is my favorite intro to neuroscience course: https://www.mcb80x.org/

It's a beautiful introduction ... Try not to drool too much! Read "A Few Useful Things to Know about Machine Learning" by Prof. Pedro Domingos. It's densely packed with valuable information, but not opaque. The author understands that there's a lot of "black art" and folk wisdom, and they invite you in. Take your time with this one.

You've probably heard it before: "a bad craftsman blames his tools." But when is the last time you saw someone building a house with a hammer, a hand saw and some 2x4s? When you build a house, you need the right tools and materials to build a house. When you build a skills, there are a different set of tools and materials. The basic ingredients for learning are neurons and myelin. Each time you fire a set of neurons while learning, they get wrapped in another thin layer of myelin, which is like insulation on an electric cord.

"As our technologies change the world, the responsibility only grows deeper for each of us to take an active role in shaping it the way we want--not the other way around."-Ray It's what we teach; it's what we expect from the world and expect our students to understand. When I was a teenager growing up in a working class town, I can recall the horror stories of robots which would take over our jobs and that would make human workers redundant….literally. It turned out that the'robots' of the time were actually automated processes and machinery with little or no sign of the robots my over active imagination conjured up after years of watching sci-fi films. Jobs were not lost en masse in my little town and humans were not rendered redundant…well not by mechanical beasts anyway.

A major challenge in the training of recurrent neural networks is the so-called vanishing or exploding gradient problem. The use of a norm-preserving transition operator can address this issue, but parametrization is challenging. In this work we focus on unitary operators and describe a parametrization using the Lie algebra $\mathfrak{u}(n)$ associated with the Lie group $U(n)$ of $n \times n$ unitary matrices. The exponential map provides a correspondence between these spaces, and allows us to define a unitary matrix using $n^2$ real coefficients relative to a basis of the Lie algebra. The parametrization is closed under additive updates of these coefficients, and thus provides a simple space in which to do gradient descent. We demonstrate the effectiveness of this parametrization on the problem of learning arbitrary unitary operators, comparing to several baselines and outperforming a recently-proposed lower-dimensional parametrization. We additionally use our parametrization to generalize a recently-proposed unitary recurrent neural network to arbitrary unitary matrices, using it to solve standard long-memory tasks.

In a Harvard Business School classroom in Boston, MA, robots are on the rise. MBA students are trying to crack a case study on the self-driving cars pioneered by Tesla, Google, and Uber. What is the potential for robots to reshape our roads? And what are the challenges and opportunities of entering that business? This is a case that David Yoffie, professor of international business administration, believes is essential reading for tomorrow's business leaders.