The almost overnight shift of resources toward remote work has introduced the need for far more flexible, dynamic and seamless end-to-end applications, putting us on a path that requires autonomous capabilities using AIOps – Artificial Intelligence for IT Operations. It’s the topic that the SNIA Cloud Storage Technologies Initiative is going to cover on August 25, 2020 at our live webcast, “IT Modernization with AIOps: The Journey.” Our AI expert, Parviz Peiravi, will provide an overview of concepts and strategies to accelerate the digitalization of critical enterprise IT resources, and help architects rethink what applications and underlying infrastructure are needed to support an agile, seamless data centric environment. This session will specifically address migration from monolithic to microservices, transition to Cloud Native services, and the platform requirements to help accelerate AIOps application delivery within our dynamic hybrid and multi-cloud world.     Join this webcast to learn:  • Use cases and design patterns: Data Fabrics, Cloud Native and the move from Request Driven to Event Driven  •    Foundational technologies supporting observability: how to build a more consistent scalable framework for governance and orchestration  •    The nature of an AI data centric enterprise: data sourcing, ingestion, processing, and distribution  This webcast will be live, so please bring your questions. We hope to see you on August 25th. Register today.

In the "Corona Summer" of 2020, Prof. Dr. Philipp Hennig remotely taught the course on Probabilistic Machine Learning within the Tübingen International Master Programme on Machine Learning. The course consists of two 90min lectures per week (26 lectures in total) plus a weekly practical / tutorial. Videos of all lectures are available on the youtube channel of the Tübingen Machine Learning Groups. The tutorials were taught by members of the Chair: Alexandra Gessner, Julia Grosse, Filip de Roos, Jonathan Wenger, Marius Hobbhahn, Nicholas Krämer, and Agustinus Kristiadi. The exercises and other material from these tutorials are available only to Tübingen students, via Ilias.

Machine learning is a concept that is as old as computers. In 1950, Alan Turing created the Turning Test. It was a test for computers to see if a machine can convince a human it is a human and not a computer. Soon after that, in 1952, Arthur Samuel designed the first computer program where a computer can learn as it ran. This program was a checker game, where the computer learned the player's patterns during the match, and then use this knowledge to improve the computer's next moves.

Infosys (NYSE: INFY), a global leader in next-generation digital services and consulting, today announced a consortium in partnership with pymetrics – the leader in fair talent matching – that brings together training partners Merit America, Per Scholas, Revature, and Woz Enterprise. The consortium will leverage Infosys Wingspan and pymetrics' AI-based talent-matching platform to meet the reskilling and employment needs raised by the COVID-19 crisis in America. Reskill and Restart--powered by Infosys Wingspan--takes job seekers on a guided journey, beginning with aptitude and skills assessment, followed by curated job-specific skills training, and culminating in matching them with available positions. The consortium of partners has built new pathways for talent to transition from traditional jobs across various industries and workstreams to digital and operations jobs of the future. It also enables employers who are scaling up to review the available talent pool for the right match and hire them while they undergo rapid and job-specific reskilling on this integrated multi-stakeholder platform.

Is this class being recorded?

In a previous article, we have discussed about what Machine Learning is and saw some of its applications. Today, we continue this series of articles, dedicated to Data Science, Machine Learning and Artificial Intelligence (AI), by discussing what SQL Server Machine Learning Services are, and how you can use them, for efficiently implementing high-quality Data Science projects in SQL Server. SQL Server Machine Learning Services, were originally released with SQL Server 2016, known as "R Services", with support for the R language. Later on, with the release of SQL Server 2017, one of its most significant features, was the enhanced support for Machine Learning which provided support for both R and Python programming languages. In this release of SQL Server, "R Services" were renamed to "Machine Learning Services".

Thanks to advancements in deep learning & artificial neural networks, computer vision is increasingly capable of mimicking human vision & is paving the way for self-driving cars, medical diagnosis, scanning recorded surveillance, manufacturing & much more. In this introductory workshop, Sage Elliot will give an overview of deep learning as it related to computer vision with a focused discussion around image classification. You will also learn about careers in computer vision & who are some of the biggest users of this technology. About Your Instructor: Sage Elliott is a Machine Learning Developer Evangelist for Sixgill with about 10 years of experience in the engineering space. He has passion for exploring new technologies & building communities.

Online Courses Udemy - Machine Learning Practical Workout 8 Real-World Projects, Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks Created by Dr. Ryan Ahmed, Ph.D., MBA Kirill Eremenko Hadelin de Ponteves SuperDataScience Team Mitchell Bouchard English [Auto] Students also bought Deployment of Machine Learning Models Machine Learning Practical: 6 Real-World Applications DataScience-Stats,MachineLearning,NLP-Python-R-BigData-Spark Deploy Machine Learning & NLP Models with Dockers (DevOps) Data Science & Deep Learning for Business 20 Case Studies Causal Data Science with Directed Acyclic Graphs Preview this course GET COUPON CODE Description "Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine/Deep Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation and technology. Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion. The more hidden layers added to the network, the more "deep" the network will be, the more complex nonlinear relationships that can be modeled. Deep learning is widely used in self-driving cars, face and speech recognition, and healthcare applications. The purpose of this course is to provide students with knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world dataset. This course covers several technique in a practical manner, the projects include but not limited to: (1) Train Deep Learning techniques to perform image classification tasks. The course is targeted towards students wanting to gain a fundamental understanding of Deep and machine learning models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master deep and machine learning models and can directly apply these skills to solve real world challenging problems."

In this tutorial, you will learn how to build an R-CNN object detector using Keras, TensorFlow, and Deep Learning. Today's tutorial is the final part in our 4-part series on deep learning and object detection: What if we wanted to train an object detection network on our own custom datasets? How can we train that network using Selective Search search? And how will using Selective Search change our object detection inference script? In fact, these are the same questions that Girshick et al. had to consider in their seminal deep learning object detection paper Rich feature hierarchies for accurate object detection and semantic segmentation. Each of these questions will be answered in today's tutorial -- and by the time you're done reading it, you'll have a fully functioning R-CNN, similar (yet simplified) to the one Girshick et al. implemented! To learn how to build an R-CNN object detector using Keras and TensorFlow, just keep reading.

We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020. The entire book is available to read online for free and the site includes video lectures and other resources. New to this edition is an entire part devoted to regression and deep learning. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.