For people curious about the Roam API and confused by the syntax, or interested in why Conor went with Datomic/Datascript and not a traditional database, this older talk by Roam developer @mark_bastian is a great overview.
You should be able to model the entire Spiderman story in Roam.
Page title: Peter Parker
Child of:: [[Richard Parker]] [[Mary Parker]]
Aliases:: [[Spidey]]
etc, and do these kind of queries.
"Show me companies in Boise, Idaho, founded by women, whose evaluation is lower than 10X ARR"
"Show me a graph of my sleep quality versus days in which I ate foods that had gluten in them or not" (where [[bread]] has a page with ingredients::).
More from Tech
These past few days I've been experimenting with something new that I want to use by myself.
Interestingly, this thread below has been written by that.
Let me show you how it looks like. 👇🏻
When you see localhost up there, you should know that it's truly an experiment! 😀
It's a dead-simple thread writer that will post a series of tweets a.k.a tweetstorm. ⚡️
I've been personally wanting it myself since few months ago, but neglected it intentionally to make sure it's something that I genuinely need.
So why is that important for me? 🙂
I've been a believer of a story. I tell stories all the time, whether it's in the real world or online like this. Our society has moved by that.
If you're interested by stories that move us, read Sapiens!
One of the stories that I've told was from the launch of Poster.
It's been launched multiple times this year, and Twitter has been my go-to place to tell the world about that.
Here comes my frustration.. 😤
Interestingly, this thread below has been written by that.
Let me show you how it looks like. 👇🏻
Recently I just refunded all Poster's sales from Gumroad. Being that said, I decided to not using that service anymore.
— Wilbert Liu \U0001f468\U0001f3fb\u200d\U0001f3a8 (@wilbertliu) November 19, 2018
Here's a little story \U0001f447\U0001f3fb
When you see localhost up there, you should know that it's truly an experiment! 😀
It's a dead-simple thread writer that will post a series of tweets a.k.a tweetstorm. ⚡️
I've been personally wanting it myself since few months ago, but neglected it intentionally to make sure it's something that I genuinely need.
So why is that important for me? 🙂
I've been a believer of a story. I tell stories all the time, whether it's in the real world or online like this. Our society has moved by that.
If you're interested by stories that move us, read Sapiens!
One of the stories that I've told was from the launch of Poster.
It's been launched multiple times this year, and Twitter has been my go-to place to tell the world about that.
Here comes my frustration.. 😤
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!
