So it turns out that Google Chrome was making everything on my computer slow *even when it wasn’t running*, because it installs something called Keystone which is basically malware.

I made a website because this shouldn’t

Wired first reported on how bad Keystone was 11 years ago when they put it into Google Earth (they seem to put it in all their popular downloads).

https://t.co/CZsj9hZ0Qt
The fact that Keystone hides itself in Activity Monitor is bizarre. (The only sign of it was excessive CPU usage of WindowServer which is a system process).
I don’t know if Google was doing something nefarious with Keystone, or a third party figured out how to (which Wired warned about). But either way, I’m not inclined to give Google-the-organization the benefit of the doubt (despite the many good people who work on Chrome)...
...since it's been a decade+ and this still hasn't been "fixed".

There is no reason for auto-update software to need to do what Chrome/Keystone was doing. It also has a long history of crashing Macs.
Chrome is bad. There is no reason it should make everything slow *when it’s not running* (it shouldn’t make everything slow when it is running either). There are other good browsers based on Chromium (Brave, Vivaldi), and Safari is fast & lightweight too.

https://t.co/Twwxir5pwF

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THREAD: How is it possible to train a well-performing, advanced Computer Vision model 𝗼𝗻 𝘁𝗵𝗲 𝗖𝗣𝗨? 🤔

At the heart of this lies the most important technique in modern deep learning - transfer learning.

Let's analyze how it


2/ For starters, let's look at what a neural network (NN for short) does.

An NN is like a stack of pancakes, with computation flowing up when we make predictions.

How does it all work?


3/ We show an image to our model.

An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.

Here is what it might look like for a black and white image


4/ The picture goes into the layer at the bottom.

Each layer performs computation on the image, transforming it and passing it upwards.


5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.

The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!

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