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.

Concepts in the book are laid out clearly, often with diagrams, but the book moves quickly. The book expects you to keep up or you will fall behind. That being said, each section has an overview of the concepts to be covered and ends with worked examples and quiz questions, the answers to which are available on the book's website. Take my free 7-day email crash course now (with sample code). Click to sign-up and also get a free PDF Ebook version of the course.

Deep learning is having a large impact on the field of natural language processing. But, as a beginner, where do you start? Both deep learning and natural language processing are huge fields. What are the salient aspects of each field to focus on and which areas of NLP is deep learning having the most impact? In this post, you will discover a primer on deep learning for natural language processing.

Whether you you need guidance on learning to code or you are a seasoned machine learning practitioner, Google is here to help. The search engine giant has come up with a new course -- 'Learn with Google AI' -- that acts as a practical introduction to machine learning to all users for free. Machine learning (ML) is a branch of artificial intelligence (AI) and pertains to a computer's ability to execute certain tasks on its own without being programmed by a human being. Some examples of ML include self-driving cars, speech recognition, language translators, etc. Google's new machine learning crash course is designed to provide a fast-paced self-study guide for aspiring machine learning practitioners using high-level TensorFlow (TF) APIs. It features a series of video lessons with lectures from ML experts, real-world case studies and hands-on practice exercises to help users learning about key ML algorithms and frameworks.

Artificial intelligence is gaining interest and ground, with major players investing heavily in areas as disparate as drone footage analysis, app development platforms and personal digital assistants. Google is investing in the educational side of AI, as well. The tech giant recently announced a free 15-hour machine learning training course, aimed at users of all experience levels (though knowledge of introductory algebra and some proficiency in programming basics and Python will come in handy). Machine Learning Crash Course (MLCC) is designed to help users develop skills in artificial intelligence and machine learning through free lessons, tutorials and hands-on exercises, the company announced. "We believe that the potential of machine learning is so vast that every technical person should learn machine learning fundamentals," wrote Barry Rosenberg, with the Google Engineering Education Team, on the Google Developers blog.

This tutorial is intended for someone who wants to understand how Recurrent Neural Network works, no prior knowledge about RNN is required. We will implement the most simple RNN model – Elman Recurrent Neural Network. To get a better understanding of RNNs, we will build it from scratch using Pytorch tensor package and autograd library. I assume that you have some understanding of feed-forward neural network if you are new to Pytorch and autograd library checkout my tutorial. An Elman network was introduced by Jeff Elman, and was first published in a paper entitled Finding structure in time.