1. [PyTorch]
PyTorch By Example
https://www.youtube.com/watch?v=FMvzxN6a5mI
In this talk, Jendrik Joerdening talks about PyTorch, what it is, how to build neural networks with it, and compares it to other frameworks.
#PyTorch
2. [React Native]
Can Flutter Animations be done in React Native
https://www.youtube.com/watch?v=8HYb6ixhLKQ
Instagram Stories using Reanimated
https://github.com/wcandillon/can-it-be-done-in-react-native/tree/master/instagram-stories
#ReactNative
3.[GraphQL]Top 5 GraphQL Predictions for 2019
Here’s some predictions of what the GraphQL community can expect in the upcoming months
https://medium.com/graphqlconf/top-5-graphql-predictions-for-2019-6f281f16fac1
#GraphQL
4. [VR] Designing Safe Spaces for Virtual Reality
Virtual Reality (VR) designers accept the ethical responsibilities of removing a user’s entire world and superseding it with a fabricated reality. These unique immersive design challenges are intensified when virtual experiences become public and socially-driven. As female VR designers in 2018, we see an opportunity to fold the language of consent into the design practice of virtual reality — as a means to design safe, accessible, virtual spaces.
https://research.fb.com/publications/designing-safe-spaces-for-virtual-reality/
#VR
5.[AI]Machine Learning at Facebook: Understanding Inference at the Edge
At Facebook, machine learning provides a wide range of capabilities that drive many aspects of user experience including ranking posts, content understanding, object detection and tracking for augmented and virtual reality, speech and text translations. While machine learning models are currently trained on customized datacenter infrastructure, Facebook is working to bring machine learning inference to the edge. By doing so, user experience is improved with reduced latency (inference time) and becomes less dependent on network connectivity. Furthermore, this also enables many more applications of deep learning with important features only made available at the edge. This paper takes a data-driven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.
#FacebookAI
