When Argho Chatterjee decided to pursue UpGrad and IIIT Bangalore's PG Program in Machine Learning and Artificial Intelligence, he knew he was diving straight into coding his own artificial neural networks, and had a fair idea that this technology could help him solve real-world problems. What came as a pleasant surprise was that he had the access to a personalised learning environment provided by the prestigious institute through its partnership with distinguished online education venture – UpGrad. The two institutes have been working seamlessly to provide learners with an advanced curriculum, projects created in collaboration with the industry experts, and tailor-made support for AI career choices. In fact, the acclaimed degree went on to help Argho make a transition to the role of a Data Scientist ( Deep Learning (AI)) at Samsung R&D with 80% CTC hike! Learning in a personalised environment under great faculty, Argho brushed up on the basics, imbibed conceptual knowledge, and acquired full-fledged knowledge of the field.

Stochastic momentum methods have been widely adopted in training deep neural networks. However, their theoretical analysis of convergence of the training objective and the generalization error for prediction is still under-explored. This paper aims to bridge the gap between practice and theory by analyzing the stochastic gradient (SG) method, and the stochastic momentum methods including two famous variants, i.e., the stochastic heavy-ball (SHB) method and the stochastic variant of Nesterov's accelerated gradient (SNAG) method. We propose a framework that unifies the three variants. We then derive the convergence rates of the norm of gradient for the non-convex optimization problem, and analyze the generalization performance through the uniform stability approach. Particularly, the convergence analysis of the training objective exhibits that SHB and SNAG have no advantage over SG. However, the stability analysis shows that the momentum term can improve the stability of the learned model and hence improve the generalization performance. These theoretical insights verify the common wisdom and are also corroborated by our empirical analysis on deep learning.

With more companies leveraging the cloud in their products and infrastructures, pursuing a career in cloud services can prove to be lucrative. However, aspiring cloud engineers will need a proper understanding of cloud concepts such as neural networks and deep learning to succeed. For $39, the Google Cloud Mastery Bundle offers courses designed to get you up to speed with the cloud and its uses. A simple way to dive into cloud mastery with no experience is by building a basic chatbot, an increasingly popular innovation companies use in their customer support roles. DialogFlow makes building them easy, and you can learn its ins and outs in the Google DialogFlow For Chatbots course.

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable. This paper proposes to build upon the proximal point algorithm when the empirical risk to minimize is not differentiable. In addition, the novel boosting approach, called accelerated proximal boosting, benefits from Nesterov's acceleration in the same way as gradient boosting [Biau et al., 2018]. Advantages of leveraging proximal methods for boosting are illustrated by numerical experiments on simulated and real-world data. In particular, we exhibit a favorable comparison over gradient boosting regarding convergence rate and prediction accuracy.

Tyson Navarro, 10, of Fremont, Calif., learns to build code using an iPad at a youth workshop at the Apple store in 2013. Code.org said a record number of female and under-represented minority students took AP computer science classes in 2018. SAN FRANCISCO -- Female, black and Latino students took Advanced Placement computer science courses in record numbers, and rural student participation surged this year, as the College Board attracted more students to an introductory course designed to expand who has access to sought-after tech skills. This year, 135,992 students took advanced placement (AP) computer science exams, a 31 percent increase from last year, according to data from the College Board, the organization that administers standardized tests that help determine college entrances as well as AP courses. Females and under-represented minorities were among the fastest growing groups.

An attendee looks at a Tifana.com Co. AI service character displayed on a screen at the Artificial Intelligence Exhibition & Conference in Tokyo, Japan, on Wednesday, April 4, 2018. The AI Expo will run through April 6. (Kiyoshi Ota/Bloomberg) It's generally accepted that as technology moves into classrooms, teachers will move, as the saying goes, "from a sage on the stage to a guide on side." That shift has rightly troubled teachers and teaching advocates who fear that educators who instruct, analyze and provide vital context will be diminished or co-opted outright by soulless, algorithm-driven tech. Generally, it's been easy to dismiss those fears in favor of some to-be-determined technology/teacher partnership.

In the last few years, machine learning applications have quietly entered every aspect of life: social media to speech recognition, radiology to retail, warfare to writing articles, coding to customer service, robotics to route optimization. During the 40 year information age, we told computers what to do. With advances in artificial intelligence, particularly machine learning, and faster processing chips we can feed computers giant data sets and they can (in narrow slivers) draw some inferences on their own. As we reported in Ask About AI, the rise of code that learns marks the beginning of a new era of augmented intelligence. It's a great opportunity for us to expand access to a great education and for young people to make a big contribution.

There is no registration fee. However, the workshop venue has limited space, so pre-registration is mandatory for every attendant (whether presenting an abstract or not). When the maximum number of attendees will have been reached, additional registrations will be placed on a waiting list. Please let us know if you have registered and then decide not to attend. Additionally, non-US citizens will need to register at least 8 weeks before the workshop, i.e., by August 1st, 2018 at the latest, in order for badges to be processed in time for the workshop. NASA civil servant and contractor employees are also expected to register in the NASA Conference Tracking System (NCTS) to attend the workshop, even for non-cost, local attendance. Visit https://ncts.nasa.gov to complete this process. The NCTS number for the NASA Goddard Workshop on Artificial Intelligence is 34572-18.

The 51-year-old artificial intelligence and robotics scientist is responsible for co-developing Google Street View, pioneering self-driving cars, founding Google X – the internet giant's secretive research lab – and revolutionising education by kickstarting massive open online courses (Moocs). His most recent project is developing flying cars. You launched your flying car company, Kitty Hawk, in 2015 backed by Google co-founder Larry Page and you have two projects in development – a personal aircraft called Flyer and an autonomous air taxi called Cora. Why do we need flying cars? The ground is getting more and more congested – we are all stuck in traffic all the time.

Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQL Server eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. You can install and run any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server.