Microsoft Cognitive Service service APIs enable the fastest route for businesses to integrate AI into their new or existing applications and systems. If you want to learn how to get started using the Microsoft Cognitive Services API, Microsoft Virtual Academy (MVA) has put together a series that dives into many of the most commonly used services. Follow the links below to view course content for each of the sections.

This presentation will review how 360º photography is rapidly changing the way DPR Construction documents both existing conditions and ongoing progress on job sites. We will discuss new workflows related to progress documentation and its benefits. For example, we'll cover scheduling of documentation on a weekly and/or milestone basis to enable virtual quality assurance/quality control walks with architects, engineers, and inspectors. We'll also review workflows for capturing conversations that revolve around actual project locations to assist with radio frequency interference (RFI) creation. We will discuss use for risk mitigation, including documenting existing conditions for design planning/bidding, as well as capture of MEP (mechanical, electrical, and plumbing) rough-in before dry-wall and ceiling close up.

We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gra- dient (SUCAG), for finite sum optimization problems. SUCAG constitutes an unbiased total gradient tracking technique that uses Hessian information to accelerate convergence. We an- alyze our method under the general asynchronous model of computation, in which functions are selected infinitely often, but with delays that can grow sublinearly. For strongly convex problems, we establish linear convergence for the SUCAG method. When the initialization point is sufficiently close to the optimal solution, the established convergence rate is only dependent on the condition number of the problem, making it strictly faster than the known rate for the SAGA method. Furthermore, we describe a Markov-driven approach of implementing the SUCAG method in a distributed asynchronous multi-agent setting, via gossiping along a random walk on the communication graph. We show that our analysis applies as long as the undirected graph is connected and, notably, establishes an asymptotic linear convergence rate that is robust to the graph topology. Numerical results demonstrate the merit of our algorithm over existing methods.

The Madrid ASDM summer school is in its thirteenth edition this year, with hundreds of students from all over the world having attended so far. It comprises 12 intensive (15 lecture hours) week-long courses, and a student may attend from one up to six courses. The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series. Each course has theoretical and practical classes, the latter done with R or python. While the summer school is mainly attended by people from academia - PhD students and researchers-, people from the industry also assist.

Numerical linear algebra is concerned with the practical implications of implementing and executing matrix operations in computers with real data. It is an area that requires some previous experience of linear algebra and is focused on both the performance and precision of the operations. In this post, you will discover the fast.ai Computational Linear Algebra for Coders Review Photo by Ruocaled, some rights reserved. The course "Computational Linear Algebra for Coders" is a free online course provided by fast.ai.

Unified Service Desk (USD) for Microsoft Dynamics 365 provides a configurable framework for quickly building applications for call centers so that agents can get a unified view of the customer data stored in Microsoft Dynamics 365. This webinar presents business application of Machine Learning to enhance Microsoft Dynamics 365 Unified Service Desk and build a smarter USD solution. Specifically, the following Machine Learning algorithms are explored: Case classification into different categories with a multi-class classifier, User-generated content filtering with a content moderator, Related service offering using a clustering process.

Linear algebra is a field of applied mathematics that is a prerequisite to reading and understanding the formal description of deep learning methods, such as in papers and textbooks. Generally, an understanding of linear algebra (or parts thereof) is presented as a prerequisite for machine learning. Although important, this area of mathematics is seldom covered by computer science or software engineering degree programs. In this post, you will discover the crash course in linear algebra for deep learning presented in the de facto textbook on deep learning. Linear Algebra for Deep Learning Photo by Quinn Dombrowski, some rights reserved.

R is one of the most popular languages for statistical analysis, data science, and reporting. At Dataquest, we have been adding R courses (you can learn more in our recent update). In this tutorial, we'll teach you the basics of R by building a simple grade calculator. While we do not assume any R-specific knowledge, you should be familiar with general programming concepts. This tutorial is based on part of our newly released introductory R course.

Artificial intelligence and machine learning are making their way into all aspects of our lives and businesses. Every time you ask Amazon's Alexa for the weather forecast or book a car through Lyft, you're benefitting from the power of AI. Entrepreneurs, in particular, are seeing their companies transformed by these technologies, and that trend will only continue in the coming years. One obvious opportunity for leveraging AI and machine learning in your business lies in teaching new employees about their responsibilities and the company. Many businesses already use online training programs and simulators when onboarding new employees.

Industrial robots are primarily known from the automotive industry's production lines. The goal of this class is to present robots instead as multifunctional and flexible interfaces between the digital and the physical world that can be used for anything from innovative, large-scale fabrication to immersive virtual reality (VR) simulators. This extension beyond the robots' initial scope is enabled by new software developments that facilitate a seamless workflow from design to machine through Dynamo software and KUKA prc. Utilizing parametric design tools lets us use robots for mass customization and small lot sizes, rather than mass fabrication. The class will provide an overview on how to utilize industrial robots through Dynamo and Fusion 360 software, and present realized projects by both small to medium-size enterprises as well as international corporations.