In this article, we revisit the most fundamental statistics theorem, talking in layman terms. We investigate a special but interesting and useful case, that is not discussed in textbooks, data camps, or data science classes. This article is part of a series about off-the-beaten-path data science and mathematics, offering a fresh, original and simple perspective on a number of topics. Previous articles in this series can be found here and also here. The theorem discussed here is the central limit theorem. It states that if you average a large number of well behaved observations or errors, eventually, once normalized appropriately, it has a standard normal distribution.

This paper studies the performance of a recently proposed preconditioned stochastic gradient descent (PSGD) algorithm on recurrent neural network (RNN) training. PSGD adaptively estimates a preconditioner to accelerate gradient descent, and is designed to be simple, general and easy to use, as stochastic gradient descent (SGD). RNNs, especially the ones requiring extremely long term memories, are difficult to train. We have tested PSGD on a set of synthetic pathological RNN learning problems and the real world MNIST handwritten digit recognition task. Experimental results suggest that PSGD is able to achieve highly competitive performance without using any trick like preprocessing, pretraining or parameter tweaking.

Data Science Middle East Foundation in partnership with EVERATI running 3-day training workshop series across Middle East to get you started on your data science and machine learning journey, as you learn how to use data and science to deliver insights, value and innovation. Data Science and Machine Learning workshop is a 3-day practical training program for applied introduction to data science industry practices and models of machine learning. The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the workshop, participants will be ready to implement the machine learning algorithms using data science on their own data, and immediately generate business value. The workshop will take participants through the conceptual and applied foundations of the subject.

Amazon wants customers to be able to leave their wallets at home and still go shopping, as long as they have their smartphones on hand. The online retail giant is opening up a brick-and-mortar store in Seattle called Amazon Go, and the store uses a mix of sensors and computer learning to track what items customers pick up and to automatically add them to their Amazon accounts. Amazon identifies each customer using a QR code, which individuals scan before entering the store. The store offers groceries, ready-made food, and Amazon meal kits. Currently, Amazon Go is only accessible to the company's employees, but Amazon says it will open its doors to the public in early 2017.

We are on the cusp of a new industrial revolution defined by the blending of our digital and physical worlds. At HP we call this Blended Reality, and we believe it will surpass the revolutions that came before in scale, scope and complexity. Connected factories, robotics, artificial intelligence, augmented reality and 3D printing have the potential to disrupt and reinvent virtually every aspect of the $12 trillion manufacturing industry, unlocking unprecedented economic potential. We estimate 3D printing will expand at a 30% compound annual growth rate to an $18 billion industry in 2021, with plastics being the largest portion of the market accounting for an estimated $10.4 billion. Even at $18 billion, that is a small fraction of the $12 trillion manufacturing industry.

One day we will "think about machine learning the way we think about electricity: It's hard to imagine the world without it," said SAP Chief Innovation Officer Juergen Mueller at the recent SAP TechEd Barcelona. Under Mueller, SAP has embarked on a journey to bring machine learning to business around the world, essentially "electrifying" all applications with this technology. For the uninitiated, machine learning takes Big Data, runs it against sophisticated algorithms and helps applications to learn from this information. Massively improved computing power makes this possible in real time. Most importantly, it allows applications to "think" and independently resolve problems – going beyond what they were explicitly programmed to do, and often what humans can do.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence.

Meet SALTO: a powerful new wall-jumping robot built by researchers at UC Berkeley. According to SALTO's makers, the diminutive, one-legged hopper not only has the "highest robotic vertical jumping agility ever recorded," but also the ability to link together multiple jumps in quick succession. SALTO stands for saltatorial locomotion on terrain obstacles, and the motion of the mechanical jumping leg was modeled after galagos -- small jumping primates native to Africa that have stretchy tendons in their legs that allow them to store energy and jump with more force than if they only used their leg muscles alone. The galago is so agile not only because it can make a big leap, but also because it can essentially wind up its legs into a crouched position in mid-flight and immediately leap again upon landing. At just 100 grams and 26 centimeters (10.2 inches) tall when fully extended, SALTO can jump a little bit more than one meter (3.3 feet) high in a single leap.

Edtech, from artificial intelligence to cloud computing and augmented reality, is sweeping through the higher education sector. And as online learning providers continue to challenge the oldest and best-known institutions, the world's top business schools are racing to adopt cutting-edge learning technologies. "Innovations by vendors have led to the use of more advanced technologies such as simulations, cloud-based solutions, and AR," says Jhansi Mary, lead analyst at Technavio, the consultancy. At the forefront of the innovation wave is artificial intelligence, which has already permeated social media, stock picking and even transport. Advocates say AI can be applied in education through the tracking of students, to predict and optimize learning and performance.

In this article, we revisit the most fundamental statistics theorem, talking in layman terms. We investigate a special but interesting and useful case, that is not discussed in textbooks, data camps, or data science classes. This article is part of a series about off-the-beaten-path data science and mathematics, offering a fresh, original and simple perspective on a number of topics. Previous articles in this series can be found here and also here. The theorem discussed here is the central limit theorem. It states that if you average a large number of well behaved observations or errors, eventually, once normalized appropriately, it has a standard normal distribution.