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"His brain didn't have room to grow in his head, and it was compressed, I could tell: I would hold him, and he would just start twitching in my arms," she says.
Essentially, you could think of this as an example, or a generalization, rather, of the notion that if, let's say I had a signal that was recorded on a tape player, and if I play the tape back at, let's say, twice the speed, which means that I'm compressing the time axis linearly by a factor of 2.
Please, bear in mind that I am compressing into a few paragraphs a rich and dense history.
In other words, the features of (x_l^{(i)}) and (x_t^{(i)}) are compressed and represented in terms of these learned features.
With Speak Out, the process was compressed to 11 weeks.
The left lung was compressed.
The underlying lung was compressed.
Intermediately data was compressed via hadoop-lzo.
Sound was compressed with mp3 codec.
In other words, the features of training vector (x_l^{(i)}) are compressed and represented in terms of these learned features.
It's like I'm in a locked room and I can't get out and the walls are closing in and in and I'm being compressed and destroyed under the pressure".
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com