Stan Leong describes Mozilla's activities in Emerging Markets.

Singapore-based developer advocate Hui Jing Chen opens her CSS Subgrid talk in Korean (presentation in English).

New Zealand based Research Engineering manager Phil Lamb describes Mozilla's Mixed Reality offering and the web platform that's underlying.

Head of Audience Insights at Mozilla, Venetia Tay describes the research her team has done to arrive at their user-centric perspective on privacy in the browser.

A presentation by Singapore-based Grab engineer Sriraghavan Subramanian describes optimization of single page apps using techniques from the backend.

Karl Dubost introduces webcompat.com and speaks about the challenges of browser compatibility. His Mozilla colleague Daisuke Akatsuka speaks about Firefox DevTools and a browser compatibility-checker tool that's under development.

Kathy Giori introduces Mozilla WebThings, which offers a private, secure, and interopable IoT solution that's not dependent on storage in the cloud.

Brian Birtles shares his pet peeves about web animation, and offers some technological solutions.

An overview of what we are working on at Mozilla - from new Firefox releases to WASM and much more! Ali Spivak is the Director of Developer Relations at Mozilla, and leads Mozilla’s developer documentation (MDN), content, events, and community participation initiatives.

In part four of Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoron[email protected]) discusses the build of an image classifier for rock, paper, and scissors. In episode one, we showed a scenario of rock, paper, and scissors; and discussed how difficult it might be to write code to detect and classify these. As the episodes have progressed into machine learning, we’ve learned how to build neural networks from detecting patterns in raw pixels, to classifying them, to detecting features using convolutions. In this episode, we have put all the information from the first three parts of the series into one. Links: Colab notebook →http://bit.ly/2lXXdw5 Rock, paper, scissors dataset → http://bit.ly/2kbV92O This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Watch more Coding TensorFlow → http://bit.ly/2lytA4j Subscribe to the TensorFlow channel → http://bit.ly/2ZtOqA3

In part three of Machine Learning Zero to Hero, AI Advocate Laurence Moroney ([email protected]) discusses convolutional neural networks and why they are so powerful in Computer vision scenarios. A convolution is a filter that passes over an image, processes it, and extracts features that show a commonality in the image. In this video you'll see how they work, by processing an image to see if you can extract features from it! Codelab: Introduction to Convolutions → http://bit.ly/2lGoC5f This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Watch more Coding TensorFlow → http://bit.ly/2lytA4j Subscribe to the TensorFlow channel → http://bit.ly/2ZtOqA3

In part two of Machine Learning Zero to Hero, AI Advocate Laurence Moroney ([email protected]) walks through basic computer vision with machine learning by teaching a computer how to see and recognize different objects. Beyond Hello World, a Computer Vision Example: https://goo.gle/34cHkDk This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Watch more Coding TensorFlow → https://bit.ly/Coding-TensorFlow Subscribe to the TensorFlow channel → http://bit.ly/2ZtOqA3

Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself. But what does ML actually look like? In part one of Machine Learning Zero to Hero, AI Advocate Laurence Moroney ([email protected]) walks through a basic Hello World example of building an ML model, introducing ideas which we'll apply in later episodes to a more interesting problem: computer vision. Try this code out for yourself in the Hello World of Machine Learning → https://goo.gle/2Zp2ZF3 This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Watch more Coding TensorFlow → https://bit.ly/Coding-TensorFlow Subscribe to the TensorFlow channel → http://bit.ly/2ZtOqA3

New technologies have fundamentally changed the way we make and experience music. In this session Claire Evans, artist, author and one half of the pop duo YACHT talks about deep learning as a tool in their creative process. Their new album explores Google AI’s research project, Magenta, an open-source music-making package using machine learning models. Watch more #io19 here: Inspiration at Google I/O 2019 Playlist → https://goo.gle/2LkBwCF Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the Google Developers Channel → https://goo.gle/developers Get started at → https://developers.google.com/ Speaker(s): Claire Evans, Adam Roberts, Jesse Engel T1AFC0

Another episode of "code this 💪, not that 💩" looking at ten important concepts in RxJS and how to avoid common anti-patterns. https://fireship.io - Source Code https://github.com/fireship-io/175-code-this-not-that-rxjs - ReactiveX http://reactivex.io/ - print method code https://gist.github.com/codediodeio/ed900f4cf5752b54160a82bc3ab2d9ea #RxJS #JavaScript