This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.

Throughout my career, I have been investigating how new technology and research results can be of benefit for the average user. Over the years I worked with cutting edge technology (Artificial Intelligence, Semantic Web, Web 2.0, mobile applications) and investigated its potential to be employed in daily life, by non-experts. I have years of expertise in coordinating international teams. I like to talk about and present latest technology and research results to laymen, for instance at Barcamps and as an invited speaker at innovation fairs. Throughout my career, I have been investigating how new technology and research results can be of benefit for the average user.

Last week I had the opportunity to visit my graduate school alma mater, The University of Arizona where I studied artificial intelligence and image processing many years ago. I remember signing up for my first semester classes and electing to challenge myself with Professor Neifeld's neural network class. It already had the reputation of being one of the toughest classes requiring students to understand both the mathematical theory and real-world application of neural networks to solve classification and other problems. Neural Networks Before Cloud Computing Of course back then there wasn't cloud computing or easy access to parallel computing methods or deep learning Python libraries. As students, we had to program the algorithms by hand starting with the mathematics of a single neuron, the iterations to loop through all the neurons in each layer, and the algorithms to implement the backpropagation learning algorithms.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. What Level of Maths Do You Need?

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framework for updating stochastic gradient estimates; when comparing to SAG/SAGA, SARAH does not require a storage of past gradients. The linear convergence rate of SARAH is proven under strong convexity assumption. We also prove a linear convergence rate (in the strongly convex case) for an inner loop of SARAH, the property that SVRG does not possess. Numerical experiments demonstrate the efficiency of our algorithm.

The robot is building a tesseract. He motions at a glowing cube floating before him, and an identical cube emerges. He drags it to the left, but the two cubes stay connected, strung together by glowing lines radiating from their corners. The robot lowers its hands, and the cubes coalesce into a single shape--with 24 square faces, 16 vertices, and eight connected cubes existing in four dimensions. And the robot is Brian Greene, a physicist at Columbia University and bestselling author of several popular science books.

Machine translation systems that convert sign language into text and back again are helping people who are deaf or have difficulty hearing to communicate with those who cannot sign. KinTrans, a start-up based in Dallas, Texas, is trialling its technology in a bank and government offices in the United Arab Emirates, and plans to install it in more places over the next couple of months. SignAll, a company based in Budapest, Hungary, will begin its own trials next year. KinTrans uses a 3D camera to track the movement of a person's hands as they sign words. A sign language user can approach a bank teller and sign to the KinTrans camera that they'd like assistance, for example.

Painful exercises in basic arithmetic are a vivid part of our elementary school memories. A multiplication like 3,752 6,901 carried out with just pencil and paper for assistance may well take up to a minute. Of course, today, with a cellphone always at hand, we can quickly check that the result of our little exercise is 25,892,552. Indeed, the processors in modern cellphones can together carry out more than 100 billion such operations per second. What's more, the chips consume just a few watts of power, making them vastly more efficient than our slow brains, which consume about 20 watts and need significantly more time to achieve the same result. Of course, the brain didn't evolve to perform arithmetic.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

From assembly lines to cubicles, this word strikes fear into the heart of workers the world over. However, automation's effect on creative professionals (and designers in particular) is somewhat unclear: While some writers warn of a day in the near future when computers will automate creativity, still others hypothesize that creativity may be our best (and last) defense against the widespread loss of jobs. So which will it be? Should designers fear the machines? Or should they embrace them?