Machine learning is a hot topic for developers, but where can one learn about how to use the technology? A lot depends on your current background and your long-term goals. I have already written about the basic differences between machine-learning techniques, but this was done at a relatively high level. Getting into the details can range from learning about machine-learning methodologies at an abstract level to examining deep-learning frameworks used to develop applications. Here, we'll take a more detailed look at some of the online resources available to you, and include links to websites with much more information about machine-learning classes, frameworks, and resources.

"Traditionally, jobs like these have been a vehicle for social mobility," Sutton Trust research manager Carl Cullinane tells The Verge. These include so-called "soft skills" like confidence, motivation, communication, and resilience. The Sutton Trust report also says that there is some reason to be optimistic about the coming wave of automation, particularly if governments can encourage people to train for STEM professions (those involving science, technology, engineering, and mathematics). "From a social mobility perspective there are two important things about the STEM sector," says Cullinane of the UK job market.

Most economists agree that advances in robotics and AI over the next few decades are likely to lead to significant job losses. But what's less often considered is how these changes could also impact social mobility. A new report from UK charity Sutton Trust explains the danger, noting that unless governments take action, the next wave of automation will dramatically increase inequality within societies, further entrenching the divide between rich and poor. The are a number of reasons for this, say the report's authors, including the ability of richer individuals to re-train for new jobs; the rising importance of "soft skills" like communication and confidence; and the reduction in the number of jobs used as "stepping stones" into professional industries. For example, the demand for paralegals and similar professions is likely to be reduced over the coming years as artificial intelligence is trained to handle more administrative tasks.

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. Let's kick off our June review by looking at some great new robotics research and development in action: Inspired by arthropod insects and spiders, Harvard Professor George Whitesides and Alex Nemiroski--a former postdoctoral fellow in Whitesides' Harvard lab--have created a type of semi-soft robot capable of standing and walking. The team also created a robotic water strider capable of pushing itself along the liquid surface. The robots are described in a recently published paper in the journal Soft Robotics.

Today's highlighted deal comes via our Online Courses section of the Neowin Deals store, where you can save 95% off* this Complete iOS 11 Machine Learning Masterclass. Become a master of smart app development. Ready to start building professional, career-boosting mobile apps? Machine learning is one of the fastest growing fields in tech and many apps are starting to integrate machine learning to add a layer of intelligence. Only the smart will survive. In this comprehensive course, you'll learn how to build apps for the brand new iOS 11 while incorporating machine learning, turning them into smart apps that will interact and learn from users to enhance the user experience.

While only parts of the original ban are currently in effect, the president and his minions have damaged the United States' reputation as a place that welcomes scientists, engineers, and technologists (and admittedly, pretty much everyone else in the world). In May, a coalition of academic and educational groups raised concerns about plans for "enhanced vetting," writing in a letter to the State Department, "We are very concerned that if the proposed changes are implemented, international undergraduate and graduate students, scholars, and scientific collaborators may be discouraged from coming to the United States." A March report said that 40 percent of colleges had seen declines in applications from international students.

I have been using Tensorflow since my enrollment of Udacity deep learning Nanodegree. I have done a few projects using Tensorflow. Honestly speaking, it has been quite a while. I still have been constantly confused by Tensorflow's weird syntax and structures. Therefore, I decided to start over again.

Artificial intelligence is coming to a classroom near you -- and not as a replacement for teachers. Recent research predicts that the use of AI in the education sector will grow 47.5 percent through 2021, eSchool News reports. Of all the areas where AI might work in K–12, the article indicates the potential to create adaptive learning features that personalize tools for each student's learning experience is the biggest. "With some simple input, AI mechanisms can assemble all sorts of narratives for students -- about their future, job applications, and ways to make an impact down the road -- to inform pursuits now that will lead to desired results," says Ralph Lucci, co-founder of Behavior Design, in the article. Some major ed tech providers have already brought AI into education by adding elements of machine learning into their tools.

Get free deep learning resources: https://goo.gl/Z6vLDU Walk through several examples, and learn how to decide which method to use. The video outlines the specific workflow for solving a machine learning problem. The video also outlines the differing requirements for machine learning and deep learning. You'll learn about the key questions to ask before deciding between machine learning and deep learning.

The recent success of neural networks in applications such as speech recognition, vision and autonomous navigation has led to great excitement by members of the artificial intelligence (AI) community and the general public at large. Over a relatively short period, by the science clock, we managed to automate some tasks that have defied us for decades and using one of the more classical techniques coming out of artificial intelligence research. The triumph over these achievements has led some to describe the automation of these tasks as having reached human level intelligence. This perception, originally hinted at in academic circles, has been gaining momentum more broadly and is leading to some implications. For example, a trend is emerging in which machine learning research is being streamlined into neural network research, under its newly acquired label "deep learning." This perception has also caused some to question the wisdom of continuing to invest in other machine learning approaches, or even mainstream areas of artificial intelligence, such as knowledge representation, symbolic reasoning and planning. Some coverage of AI in public arenas, particularly comments made by some visible figures, has led to mixing this excitement with fear of what AI may be bringing us in the future (i.e., doomsday scenarios).