The entire discussion around Facebook’s disclosures of what happened in 2016 is very frustrating. No exec stopped any investigations, but there were a lot of heated discussions about what to publish and when.
The story doesn\u2019t say you were told not to... it says you did so without approval and they tried to obfuscate what you found. Is that true?
— Sarah Frier (@sarahfrier) November 15, 2018
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1/ 👋 Excited to share what we’ve been building at https://t.co/GOQJ7LjQ2t + we are going to tweetstorm our progress every week!
Week 1 highlights: getting shortlisted for YC W2019🤞, acquiring a premium domain💰, meeting Substack's @hamishmckenzie and Stripe CEO @patrickc 🤩
2/ So what is Brew?
brew / bru : / to make (beer, coffee etc.) / verb: begin to develop 🌱
A place for you to enjoy premium content while supporting your favorite creators. Sort of like a ‘Consumer-facing Patreon’ cc @jackconte
(we’re still working on the pitch)
3/ So, why be so transparent? Two words: launch strategy.
jk 😅 a) I loooove doing something consistently for a long period of time b) limited downside and infinite upside (feedback, accountability, reach).
cc @altimor, @pmarca
4/ https://t.co/GOQJ7LjQ2t domain 🍻
It started with a cold email. Guess what? He was using BuyMeACoffee on his blog, and was excited to hear about what we're building next. Within 2w, we signed the deal at @Escrowcom's SF office. You’re a pleasure to work with @MichaelCyger!
5/ @ycombinator's invite for the in-person interview arrived that evening. Quite a day!
Thanks @patio11 for the thoughtful feedback on our YC application, and @gabhubert for your directions on positioning the product — set the tone for our pitch!
Week 1 highlights: getting shortlisted for YC W2019🤞, acquiring a premium domain💰, meeting Substack's @hamishmckenzie and Stripe CEO @patrickc 🤩
2/ So what is Brew?
brew / bru : / to make (beer, coffee etc.) / verb: begin to develop 🌱
A place for you to enjoy premium content while supporting your favorite creators. Sort of like a ‘Consumer-facing Patreon’ cc @jackconte
(we’re still working on the pitch)
3/ So, why be so transparent? Two words: launch strategy.
jk 😅 a) I loooove doing something consistently for a long period of time b) limited downside and infinite upside (feedback, accountability, reach).
cc @altimor, @pmarca
4/ https://t.co/GOQJ7LjQ2t domain 🍻
It started with a cold email. Guess what? He was using BuyMeACoffee on his blog, and was excited to hear about what we're building next. Within 2w, we signed the deal at @Escrowcom's SF office. You’re a pleasure to work with @MichaelCyger!
5/ @ycombinator's invite for the in-person interview arrived that evening. Quite a day!
Thanks @patio11 for the thoughtful feedback on our YC application, and @gabhubert for your directions on positioning the product — set the tone for our pitch!
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!