Machine learning is one of the hottest skills in the upcoming years. We have advanced it very rapidly in the strong age of AI. A major number of developers are looking towards acquiring machine learning and deep learning skills. And if you are one of these people then you might get confused when it comes to choosing the right programming language for machine learning. Coding languages are becoming more versatile and each one is unique by presenting specific features which you will not find anywhere else. You only need to find the information, analyze it and then sample any one or another language to decide which is the best one or which one fulfills your needs?

There is a growing demand for data-labelling services that are "localised"- both linguistically and culturally relevant to India From an opportunity point of view, there are about a lakh jobs posted on various portals currently There is a growing demand for data-labelling services that are "localised"- both linguistically and culturally relevant to India NEW DELHI: Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars. All Mathi knows is that it makes her life easier.

During the Parkland, Florida, school shooting in 2018, the shooter was caught on a security camera pulling his rifle out of a duffle bag in the staircase 15 seconds before discharging the first round. However, the School Resource Officer didn't enter the building because he wasn't confident about the situation, and the Coral Springs Police Department had no idea what the shooter even looked like until 7 minutes and 30 seconds after the first round was fired. If the video system had included technology to recognize the gun threat in real time, alerts could have been sent to the security team. An announcement could have been made right away for all students and faculty in Building 12 to barricade their doors, and law enforcement could have responded a lot faster to a real-time feed of timely and accurate information. Aegis AI offers such a technology, which the company says enables existing security cameras to automatically recognize gun threats and notify security in real-time.

In an attempt to revamp the education system at a primary level, the Central Board of Secondary Education (CBSE) has signed partnerships with Microsoft India and IBM to focus on tech reskilling for teachers and AI curriculum in schools. In its partnership with Microsoft India, CBSE is looking to conduct capacity building programmes for high school teachers with an aim to integrate cloud-powered technology in K12 teaching and inculcating digital teaching skills in educators through curriculum as well as extra-curricular training. The programme for teachers of grades VIII to X will be conducted in 10 cities across the country, starting September 11. The CBSE Microsoft association is expected to provide teachers better access to the latest information and communication technology (ICT) tools and help them integrate technology into teaching and the curriculum in a smart manner. The selected 1000 teachers nominated by CBSE will be undergoing a three-day project-based training for practical, hands-on knowledge of Microsoft 365 tools such as OneNote, Flipgrid, Teams, Outlook, Minecraft and Paint3D.

Prominent digital companies such as Google, Amazon, and Netflix are making tremendous strides in the machine learning landscape, and this means the tide is drawing everyone toward the machine learning horizon. Therefore, you would be doing yourself a favor by mastering the field of machine learning. Soon, the phrase "I know a little about machine learning" won't cut it; experts and knowledgeable engineers are needed to usher in the future of machine learning. We have tips to help you become a top-notch machine learning engineer so you're not left alone on the shore. Thankfully, the emergent push toward more sophisticated machine learning systems has led many engineers and academics to learn the basics of machine learning systems, and many qualified individuals offer classes about machine learning on the internet.

The vast potential of AI to improve efficiencies and provide optimized solutions along with machine learning in supply chain boosts organization management capabilities. These technologies not only enable quick response and faster query resolution but also are greatly adaptable to change when exposed to new data. Some of the key players of the supply chain analytics market are JDA Software Group, Inc.; Aera Technology; Accenture PLC; Birst, Inc.; Genpact Limited; Capgemini SA; IBM; Kinaxis; Manhattan Associates; Lockheed Martin Corporation; and Maersk Group. These companies are inclined to develop new portfolio of solutions designed to help build a connected supply chain and allow easy access to users thereby enabling faster decisions.

India News: Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children.

When Norio Ichihashi is involved in a project, anyone with any sense had better pay attention. After all, Ichihashi is one of the co-founders of Mobileye (MBLY), which was just recently purchased by Intel for $15.3 billion. Ichihashi now has his sights set on a new venture: a new artificial intelligence educational gaming app called Zangu that enables children to learn a new language in as little as 30 days. "There are serious problems with the way children learn a second language," says Kenichi Kainuma, the CEO of AI Teach U Shanghai, which created Zangu, along with Ichihashi. "There's always been problems with children – and even adults – learning a new or a second language."

Incorporating second order curvature information in gradient based methods have shown to improve convergence drastically despite its computational intensity. In this paper, we propose a stochastic (online) quasi-Newton method with Nesterov's accelerated gradient in both its full and limited memory forms for solving large scale non-convex optimization problems in neural networks. The performance of the proposed algorithm is evaluated in Tensorflow on benchmark classification and regression problems. The results show improved performance compared to the classical second order oBFGS and oLBFGS methods and popular first order stochastic methods such as SGD and Adam. The performance with different momentum rates and batch sizes have also been illustrated. Keywords: Neural networks · stochastic method · online training · Nesterov's accelerated gradient · quasi-Newton method · limited memory · Tensorflow 1 Introduction Neural networks have shown to be effective in innumerous real-world applications.