Native apps often feature transitions between states that both look great and help communicate the type of navigation to the user. The bad news: creating transitions between pages on the web is impossible. In-page transitions are possible, but complex. The good news: new APIs are coming to simplify this process, building on top of CSS animations and the web animation API, and it works across navigations! Resources:
Demo → https://goo.gle/3M55GV8 (requires document-transition and enable-experimental-web-platform-features flags)
Developer guide → https://goo.gle/38hsYIU
Full explainer → https://goo.gle/3yrbzrS
Codelab → https://goo.gle/39cXoMw Speaker: Jake Archibald PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=JCJUPJ_zDQ4 https://www.youtube.com/watch?v=85baheg2wmI

Sidero Metal delivers Kubernetes on bare metal. Easily, securely, repeatably. Walk in to a datacenter with just a laptop, and in minutes turn a rack of servers into secure, production ready, bare metal Kubernetes clusters. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=q59KLqvjQXU https://www.youtube.com/watch?v=CwGhFFvS-xQ

Check out Krishnendu Chaudhury's book 📖 Math and Architectures of Deep Learning | http://mng.bz/K2ag 📖 For 40% off this book use the ⭐ DISCOUNT CODE: twitchaud40 ⭐ Learn the most important tools in the repertoire of a data scientist and machine learning practitioner – Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Latent Semantic Analysis (LSA) – with the help of Krishnendu Chaudhury, a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems. 📚📚📚
Math and Architectures of Deep Learning | http://mng.bz/K2ag
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Math and Architectures of Deep Learning sets out the foundations of DL in a way that’s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. https://www.youtube.com/watch?v=n0RLz0rJ8gI