[1/4] Ok this is really funny, check this out.
I was in the process of booking a flight via @OneTravel. Trying to make me book ASAP, they claimed: "38 people are looking at this flight".
Whoa, 38 is a lot, I have to hurry up. But first I have to check how they came up with 38 >>

[2/4] Right click and a quick "inspect" on the number, I found out the element's class name is "view_notification_random".
Awesome variable naming guys.
So you're _randomly_ trying to freak me out. Alright >>
[3/4] So what's your sophisticated pseudo-random algorithm?
Apparently, OneTravel are choosing a number between 28 and 45.
Because as you all know, based on serious psychological research, these numbers tend to make people book their flights fast #sarcasm #not42 >>
[4/4] Here's a bonus graph.
Thanks @IddoYadlin and #WolframAlpha.

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I could create an entire twitter feed of things Facebook has tried to cover up since 2015. Where do you want to start, Mark and Sheryl? https://t.co/1trgupQEH9


Ok, here. Just one of the 236 mentions of Facebook in the under read but incredibly important interim report from Parliament. ht @CommonsCMS
https://t.co/gfhHCrOLeU


Let’s do another, this one to Senate Intel. Question: “Were you or CEO Mark Zuckerberg aware of the hiring of Joseph Chancellor?"
Answer "Facebook has over 30,000 employees. Senior management does not participate in day-today hiring decisions."


Or to @CommonsCMS: Question: "When did Mark Zuckerberg know about Cambridge Analytica?"
Answer: "He did not become aware of allegations CA may not have deleted data about FB users obtained through Dr. Kogan's app until March of 2018, when
these issues were raised in the media."


If you prefer visuals, watch this short clip after @IanCLucas rightly expresses concern about a Facebook exec failing to disclose info.
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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MDZS is laden with buddhist references. As a South Asian person, and history buff, it is so interesting to see how Buddhism, which originated from India, migrated, flourished & changed in the context of China. Here's some research (🙏🏼 @starkjeon for CN insight + citations)

1. LWJ’s sword Bichen ‘is likely an abbreviation for the term 躲避红尘 (duǒ bì hóng chén), which can be translated as such: 躲避: shunning or hiding away from 红尘 (worldly affairs; which is a buddhist teaching.) (
https://t.co/zF65W3roJe) (abbrev. TWX)

2. Sandu (三 毒), Jiang Cheng’s sword, refers to the three poisons (triviṣa) in Buddhism; desire (kāma-taṇhā), delusion (bhava-taṇhā) and hatred (vibhava-taṇhā).

These 3 poisons represent the roots of craving (tanha) and are the cause of Dukkha (suffering, pain) and thus result in rebirth.

Interesting that MXTX used this name for one of the characters who suffers, arguably, the worst of these three emotions.

3. The Qian kun purse “乾坤袋 (qián kūn dài) – can be called “Heaven and Earth” Pouch. In Buddhism, Maitreya (मैत्रेय) owns this to store items. It was believed that there was a mythical space inside the bag that could absorb the world.” (TWX)