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The following tables list the ports that must be open. To enable devices and apps to communicate with XenMobile, you open specific ports in your firewalls. Integrate XenMobile Mobile Device Management with Cisco Identity Services Engine Sending group enrollment invitations in XenMobileĬonfiguring an on-premises Device Health Attestation serverĬonfiguring certificate-based authentication with EWS for Secure Mail push notifications Role-Based Access Control and XenMobile Support Reference Architecture for On-Premises Deployments SSO and Proxy Considerations for MDX Apps Integrating with Citrix Gateway and Citrix ADC On-premises XenMobile interaction with Active Directory Use Citrix Content Collaboration with XenMobileĮndpoint Management connector for Exchange ActiveSyncĬitrix Gateway connector for Exchange ActiveSync Virtual Apps and Desktops through Citrix Secure Hub XenMobile Store and Citrix Secure Hub branding Windows Information Protection device policy Provisioning profile removal device policy
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Legacy Android Enterprise for Google Workspace (formerly G Suite) customersĭeploy devices through the Apple Deployment ProgramĪndroid Enterprise managed configurations policyĬopy Apps to Samsung Container device policyĭelete registry keys and values device policy Migrate from device administration to Android Enterprise SAML for single sign-on with Citrix Files
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Getting started workflows for XenMobile consoleĭomain or domain plus security token authenticationĬlient certificate or certificate plus domain authentication
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Release notes for XenMobile Server 10.13 Rolling Patch 3 Release notes for XenMobile Server 10.12 Rolling Patch 8
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The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.Release notes for XenMobile Server 10.13 Rolling Patch 4 (iii) Best practices in machine learning (bias/variance theory innovation process in machine learning and AI).
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(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Many researchers also think it is the best way to make progress towards human-level AI. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is the science of getting computers to act without being explicitly programmed.