Why was @LudwigAhgren's Twitch stream yesterday a masterpiece in creating live content?
(a trending piece of content right now, with in-jokes with the Twitch community and funny content that can be repurposed for YouTube).
He engaged viewers into chat by asking them to vote directly in chat temporarily, which perfectly broke up what could have been a 'boring' presentation (spoiler, it wasn't boring).
THEN.... we get a PogChamp emote tier list of every PogChamp so far.
Then of course... Ludwig creates a tier-list of his own Ads.
G e n i u s.
Six hours of amazing content, with multiple moments built for YouTube, TikTok and Twitter.
In each segment, Lugwig used background music like you would a TV sports show, it is fantastic.
Ludwig is a genius at this and deserves to be the 42nd biggest streamer on Twitch - even though i miss the old him.
More from Tech
On Wednesday, The New York Times published a blockbuster report on the failures of Facebook’s management team during the past three years. It's.... not flattering, to say the least. Here are six follow-up questions that merit more investigation. 1/
1) During the past year, most of the anger at Facebook has been directed at Mark Zuckerberg. The question now is whether Sheryl Sandberg, the executive charged with solving Facebook’s hardest problems, has caused a few too many of her own. 2/ https://t.co/DTsc3g0hQf
2) One of the juiciest sentences in @nytimes’ piece involves a research group called Definers Public Affairs, which Facebook hired to look into the funding of the company’s opposition. What other tech company was paying Definers to smear Apple? 3/ https://t.co/DTsc3g0hQf
3) The leadership of the Democratic Party has, generally, supported Facebook over the years. But as public opinion turns against the company, prominent Democrats have started to turn, too. What will that relationship look like now? 4/
4) According to the @nytimes, Facebook worked to paint its critics as anti-Semitic, while simultaneously working to spread the idea that George Soros was supporting its critics—a classic tactic of anti-Semitic conspiracy theorists. What exactly were they trying to do there? 5/
1) During the past year, most of the anger at Facebook has been directed at Mark Zuckerberg. The question now is whether Sheryl Sandberg, the executive charged with solving Facebook’s hardest problems, has caused a few too many of her own. 2/ https://t.co/DTsc3g0hQf
2) One of the juiciest sentences in @nytimes’ piece involves a research group called Definers Public Affairs, which Facebook hired to look into the funding of the company’s opposition. What other tech company was paying Definers to smear Apple? 3/ https://t.co/DTsc3g0hQf
3) The leadership of the Democratic Party has, generally, supported Facebook over the years. But as public opinion turns against the company, prominent Democrats have started to turn, too. What will that relationship look like now? 4/
4) According to the @nytimes, Facebook worked to paint its critics as anti-Semitic, while simultaneously working to spread the idea that George Soros was supporting its critics—a classic tactic of anti-Semitic conspiracy theorists. What exactly were they trying to do there? 5/
1. One of the best changes in recent years is the GOP abandoning libertarianism. Here's GOP Rep. Greg Steube: “I do think there is an appetite amongst Republicans, if the Dems wanted to try to break up Big Tech, I think there is support for that."
2. And @RepKenBuck, who offered a thoughtful Third Way report on antitrust law in 2020, weighed in quite reasonably on Biden antitrust frameworks.
3. I believe this change is sincere because it's so pervasive and beginning to result in real policy changes. Example: The North Dakota GOP is taking on Apple's app store.
4. And yet there's a problem. The GOP establishment is still pro-big tech. Trump, despite some of his instincts, appointed pro-monopoly antitrust enforcers. Antitrust chief Makan Delrahim helped big tech, and the antitrust case happened bc he was recused.
5. At the other sleepy antitrust agency, the Federal Trade Commission, Trump appointed commissioners
@FTCPhillips and @CSWilsonFTC are both pro-monopoly. Both voted *against* the antitrust case on FB. That case was 3-2, with a GOP Chair and 2 Dems teaming up against 2 Rs.
2. And @RepKenBuck, who offered a thoughtful Third Way report on antitrust law in 2020, weighed in quite reasonably on Biden antitrust frameworks.
3. I believe this change is sincere because it's so pervasive and beginning to result in real policy changes. Example: The North Dakota GOP is taking on Apple's app store.
Republican North Dakota legislators have introduced #SB2333, a bill that prohibits large tech companies from locking their users into a single app store or payment processor.https://t.co/PgyhgOhFAl
— Cory Doctorow #BLM (@doctorow) February 11, 2021
1/ pic.twitter.com/KZ8BMFQoPO
4. And yet there's a problem. The GOP establishment is still pro-big tech. Trump, despite some of his instincts, appointed pro-monopoly antitrust enforcers. Antitrust chief Makan Delrahim helped big tech, and the antitrust case happened bc he was recused.
5. At the other sleepy antitrust agency, the Federal Trade Commission, Trump appointed commissioners
@FTCPhillips and @CSWilsonFTC are both pro-monopoly. Both voted *against* the antitrust case on FB. That case was 3-2, with a GOP Chair and 2 Dems teaming up against 2 Rs.
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!
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
THREAD: Can you start learning cutting-edge deep learning without specialized hardware? \U0001f916
— Radek Osmulski (@radekosmulski) February 11, 2021
In this thread, we will train an advanced Computer Vision model on a challenging dataset. \U0001f415\U0001f408 Training completes in 25 minutes on my 3yrs old Ryzen 5 CPU.
Let me show you how...
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!
