IPO – SPAC raises cash and begins trading as a unit. The cash is placed in Trust.
Searching phase – After IPO, the SPAC management team looks for a company to merge with. This is typically 24 months but can be shorter depending on the SPAC
Here’s a SPAC thread covering some basics for new followers. I will pin this to my profile if you want to share to anyone interested in SPACs.
IPO – SPAC raises cash and begins trading as a unit. The cash is placed in Trust.
Searching phase – After IPO, the SPAC management team looks for a company to merge with. This is typically 24 months but can be shorter depending on the SPAC
Unit – A new SPAC IPO is in the form of a unit typically. This is denoted by a U on the end of a ticker. A unit consist of a share and a warrant (or fraction of one). The unit is broken out to the share and warrant 52 days after IPO typically.
Warrant – Terms vary by SPAC, the most common is that they convert 1 warrant to 1 share at a price of $11.5 and expire 5 years after merger date. Ratios can vary greatly by SPAC so refer to the prospectus.
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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
— 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!
