Education

Stagnation in the proportion of women in the workplace and women's declining representation in politics, coupled with greater inequality in access to health and education, offset improvements in wage equality and the number of women in professional positions, leaving the global gender gap only slightly reduced in 2018. This is according to the Forum's Global Gender Gap Report 2018, published today. According to the report, the world has closed 68% of its gender gap, as measured across four key pillars: economic opportunity; political empowerment; educational attainment; and health and survival. While only a marginal improvement on 2017, the move is nonetheless welcome as 2017 was the first year since the report was first published in 2006 that the gap between men and women widened. At the current rate of change, the data suggest that it will take 108 years to close the overall gender gap and 202 years to bring about parity in the workplace.

"I am delighted to invite you to participate in the first Big DataTLV event in Israel to focus on HealthCare.The event takes place on April 16, 2019, in the Wohl Convention Center in the heart of Innovation Nation, Israel."

In an interdisciplinary project funded by a Canadian Institute for Advanced Research (CIFAR) Catalyst grant, researchers at the University of Guelph and the University of Toronto, Mississauga combined expertise in fruit fly biology with machine learning to build a biologically-based algorithm that churns through low-resolution videos of fruit flies in order to test whether it is physically possible for a system with such constraints to accomplish such a difficult task. Fruit flies have small compound eyes that take in a limited amount of visual information, an estimated 29 units squared. The traditional view has been that once the image is processed by a fruit fly, it is only able to distinguish very broad features. But a recent discovery that fruit flies can boost their effective resolution with subtle biological tricks has led researchers to believe that vision could contribute significantly to the social lives of flies. This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: "can we model a fly brain that can identify individuals?"

The library of the future is more or less the same. That is, the branch is an actual and metaphoric Faraday cage. You enter, a node and a target, streamed at and pushed and yanked, penetrated by and extruding information, sloppy with it. And then your implants are cut off. Your watch, your glasses, jacket, underwear, your lenses, tablet, chips, your nanos--all go dry.

Remember all those classics you devoured in comp-lit class? Research shows that we retain an embarrassingly small sliver of what we read. In an effort to help college students boost that percentage, a team made up of a designer, a psychologist, and a behavioral economist at Australia's RMIT University recently introduced a new typeface, Sans Forgetica, that uses clever tricks to lodge information in your brain. The font-makers drew on the psychological theory of "desirable difficulty"--that is, we learn better when we actively overcome an obstruction. Sans Forgetica is purposefully hard to decipher, forcing the reader to focus.

This is the bite size course to learn Weka and Machine Learning. You will learn Machine Learning which is the Model and Evaluation of CRISP Data Mining Process. You will learn Linear Regression, Kmeans Clustering, Agglomeration Clustering, KNN, Naive Bayes, Neural Network in this course.

Whether people aware of it or not, artificial intelligence and machine learning have had a huge impact on human interaction, particularly in regards to machines, computers and devices. The impact can be felt across a range of industries including travel, retail and advertisement. Both Android and iOS mobile platforms have utilized this technology to create innovative and exciting new apps. How Is Machine Learning Currently Being Used? Artificial intelligence and machine learning technology are already being utilized to try and better our experiences every day.

Enrollment in artificial intelligence (AI) introductory courses in the United States grew by 3.4 times between 2012 and 2017, and introductory machine learning (ML) classes grew by five times during that same period. That's according to the latest AI Index 2018 Report, a rich collection of data intended to serve as a "comprehensive resource" for anybody interested in the field. The information was contributed by universities, companies, consultancies and associations. The report observed that ML courses are on a faster trajectory for growth than AI at this point. While the University of California Berkeley's introductory AI course grew by a little under two times between 2012 and 2017, its ML course had 6.8 times as many students.

The objective of a neural network is to have a final model that performs well both on the data that we used to train it (e.g. the training dataset) and the new data on which the model will be used to make predictions. The central challenge in machine learning is that we must perform well on new, previously unseen inputs -- not just those on which our model was trained. The ability to perform well on previously unobserved inputs is called generalization.

Neural networks and machine learning, a field of computer science that heavily relies on them to give computers the ability to learn without being explicitly programmed, seem to be everywhere these days. Scientists want to use advanced neural networks to find energy materials, the Wall Street would like to train neural networks to manage hedge funds and pick stocks, and Google has been relying on neural networks to deliver highly accurate translations and transcriptions. At the same time, powerful, consumer-grade hardware for machine learning is getting more affordable. For example, Nvidia has recently introduced its Titan V graphics card, which is targeted specifically at machine learning developers, who would like to create neural networks without paying for a special server to handle all the complex math operations involved in machine learning. With the future of machine learning looking so bright and the necessary computational resources being so available, many JavaScript developers wonder what's the easiest way how to create neural networks in JavaScript.