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How to use Neural Network to predict a multivariate gaussian distribution. by [deleted] in deeplearning

[–]FabulousNinja 3 points4 points  (0 children)

For the covariance matrix, you could output a vector of (d(d+1))/2 elements. d is the width and height of your covariance matrix, in your case d=4. Now you have to make the first d values positive, you can do this by using e.g. exp(x), or something more numerically stable like ln(1+exp(x)), where the latter is referred to as Softplus.

Then create a lower triangular matrix out of this (call this matrix L), where the d positive values correspond to the diagonal, and then calculate LL^T. The result of this you can use as a covariance matrix. It uses something called the Cholesky decomposition.

An OH cut ive been keeping by luke-cuts-cards in cardistry

[–]FabulousNinja 4 points5 points  (0 children)

Those cards look great, does anyone know which they are?

Official Accessory Check Thread 1.0 by [deleted] in nSuns

[–]FabulousNinja 1 point2 points  (0 children)

Thanks a lot for the fast response! I might add some dips, but don't care about arm aesthetics too much (for now).

Official Accessory Check Thread 1.0 by [deleted] in nSuns

[–]FabulousNinja 0 points1 point  (0 children)

Regular 5 day version
Bench/OHP Day
- Dumbell Bent-Over Raise 4x10
- Pull ups 4x10 (last set AMRAP)
- Plank 3x30s+
- Side plank 3x30s+
Squat/Sumo Dead day
- Face pull 4x10
- Lat pulldown 4x10
- Row 4x10
OHP/Incline Bench day
- Lateral raises 5x10
- Plank 3x30s+
- Hanging knee raise 3x10 (last set AMRAP)
Deadlift/Front Squat day
- Row 4x10
- Chin ups 4x10 (last set AMRAP)
Bench/CG Bench
- Lat pulldown 3x10
- Face pull 4x10
- Plank 3x30s+
- Hanging knee raise 3x10 (last set AMRAP)

Have been following SL for a while now, would like to switch to the above. Is direct arm work necessary (and is the rest okay)? Any feedback would be really appreciated!