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Andrew McElroy
Sophrinix
Cofounder & CTO of American Binary former maintainer of Try Ruby!
I bought M1 MacBook Air. It is the fastest computer I have, and I have been a
GNOME/GNU/Linux user for long time. It is obvious conclusion that I need
practical Linux desktop environment on Apple
Silicon.
Fortunately, Linux already works on Apple Silicon/M1. But how practical is it?
Since the recent release of Catalina, macOS has shipped with the ability to allow iOS/iPAD apps to run on macOS without any modification via a featureset known as Project Catalyst.
This is exciting, as writing React Native + Clojurescript apps as a target for the desktop is much more compelling than a pure Electron app (imo).
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Support embedded binaries (aka dynamic libraries) in Titanium modules and Hyperloop
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Notes for "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper
The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.
Issues With Training Deep Neural Networks
Internal Covariate shift
Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.
It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.
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Lava Lamps can be used as a source of randomness, which can be used to establish a random number generator. The output of the RNG can then be consumed by various computer applications.
Flipboard style tabgroup indicator for iOS. Drop tabIndicator.js into your Alloy lib folder, then add the module tag to your tabgroup and parameters to override the defaults. As you click on each tab, the indicator will slide across. VIDEO https://www.dropbox.com/s/cbw5e1ruksud9uo/tabindicator.mp4?dl=0
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