
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::).

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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
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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!
