How to pronounce“stocastic”in English

called stocastic approximation which is multicolor based inference I'm not going to go into these techniques but in general you can you can train these

Here's here's an example of you know I have a stocastic layer, you have a deterministic layer, you have a stocastic layer and then you generate

haven't normalized your input which creates some instability making it harder for stocastic gradient and ascent to uh uh uh work successfully.

generative process so given some latent state you just it's a neural network it's a stocastic neural network that generates the input data uh right and

don't really like a model that sort of randomly changes its output because it will if we stocastically change the masks uh what we do is we replace the

Stocastic screening gives you just a ridiculously

Um and roughly you see standard hyperparameters like say batch sizes of roughly 128 u using stocastic gradient descent with momentum usually

Why do we need stocastic systems in general?

So this is one example of a helm machine where you have a generative model which is a stocastic recurrent

So, um, backrop is and stocastic gradient descent, it's a great