Google announces scholarship program to train 1.3 lakh Indian developers in emerging technologies 43584 views Want to be a millionaire before you turn 25? Study artificial intelligence or machine learning 42912 views

A high-salt diet may spell trouble for the brain -- and for mental performance -- even if it doesn't push blood pressure into dangerous territory, new research has found. A new study has shown that in mice fed a very high-salt diet, blood flow to the brain declined, the integrity of blood vessels in the brain suffered, and performance on tests of cognitive function plummeted. But researchers found that those effects were not, as has long been widely believed, a natural consequence of high blood pressure. Instead, they appeared to be the result of signals sent from the gut to the brain by the immune system. The study, conducted by researchers at Weill Cornell Medicine in New York, was published Monday in the journal Nature Neuroscience.

DePaul University's School of Computing invites applications for a tenure-track position in data science at the rank of assistant professor. The successful candidate will be part of one of the fastest growing and most highly recognized data science programs in the country.

In this course, you'll learn about clustering and dimension reduction, the two fundamental techniques of unsupervised learning and you'll learn to apply them using Python 3 and industry standard, freely available software libraries like scikit-learn and SciPy. You're going to learn to use the fundamental tools of unsupervised learning that professional data scientists use everyday. So who is this course for? Perhaps you're an IT professional, an analyst, a scientist or an academic, and you're looking to make the transition to data science, or you're a student, and you want to learn what data science is all about. In this course I'm going to share with you not only what I learnt but also the joy and the fascination of discovering patterns in data - the wonder of finding hidden structure in datasets that seemed at first too large and too complex.

Neural programming involves training neural networks to learn programs from data. Previous works have failed to achieve good generalization performance, especially on programs with high complexity or on large domains. This is because they mostly rely either on black-box function evaluations that do not capture the structure of the program, or on detailed execution traces that are expensive to obtain, and hence the training data has poor coverage of the domain under consideration. We present a novel framework that utilizes black-box function evaluations, in conjunction with symbolic expressions that integrate relationships between the given functions. We employ tree LSTMs to incorporate the structure of the symbolic expression trees. We use tree encoding for numbers present in function evaluation data, based on their decimal representation. We present an evaluation benchmark for this task to demonstrate our proposed model combines symbolic reasoning and function evaluation in a fruitful manner, obtaining high accuracies in our experiments. Our framework generalizes significantly better to expressions of higher depth and is able to fill partial equations with valid completions.

I was traveling last fall with my boss, and we began to talk about our upcoming conference day around artificial intelligence. We came to the topic of machine learning, and I mentioned, half jokingly, that theoretically all web accessibility barriers could be automatically resolved through the proper application of machine learning. Since that day I've continued to consider the challenge, and have come across a few articles that have reinforced the theory. As our Q1 conference day is soon coming, I've decided to take a few minutes to share my thoughts. For those who don't know, web accessibility is the practice of making content and applications ("web content") accessible to those with a variety of disabilities, "...including blindness and low vision, deafness and hearing loss, learning disabilities, cognitive limitations, limited movement, speech disabilities, [and] photosensitivity" (https://www.w3.org/TR/WCAG20/).

What is machine learning / ai? How to learn machine learning in practice? Neural Networks (often referred to as deep learning) are particular interesting. But there are a few questions. To answer these questions and give beginners a guide to really understand them, I created this interesting course.

Deep reinforcement learning (deep RL) is a popular and successful family of methods for teaching computers tasks ranging from playing Go and Atari games to controlling industrial robots. But it is difficult to use a single neural network and conventional RL techniques to learn many different skills at once. Existing approaches usually treat the tasks independently or attempt to transfer knowledge between a pair of tasks, but this prevents full exploration of the underlying relationships between different tasks. When humans learn new skills, we take advantage of our existing skills and build new capabilities by composing and combining simpler ones. For instance, learning multi-digit multiplication relies on knowledge of single-digit multiplication, while knowing how to properly prepare individual ingredients facilitates cooking dishes with complex recipes.

The tour lists 20 interesting real-world machine learning problems for data science enthusiasts to learn by solving.

Linear Regression, GLMs and GAMs with R demonstrates how to use R to extend the basic assumptions and constraints of linear regression to specify, model, and interpret the results of generalized linear (GLMs) and generalized additive (GAMs) models. The course demonstrates the estimation of GLMs and GAMs by working through a series of practical examples from the book Generalized Additive Models: An Introduction with R by Simon N. Wood (Chapman & Hall/CRC Texts in Statistical Science, 2006). Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. The assumption of linearity is a critical (and limiting) characteristic. Generalized linear models (GLMs) relax this assumption of linearity.