teenaxta
teenaxta t1_j76i085 wrote
most ViT discussions or videos I saw assume you have an idea of attention and transformers
watch this video series to get an idea of attention and transformers in general and then you'll be good to go
teenaxta t1_j62mz4o wrote
Customer ID is useless so obviously it will be dropped. Now the actions he did is a bit tricky.
if actions are discrete classes, then i think you should break up the column into sub classes and then one hot encode the actions.
I cant really understand why you need LSTM here. Do you have a sequence data or any sort of temporal component ? If you have to use LSTM you can just set your sequence length to 1 and essentially use it as a NN. But that makes no sense honestly. Would be much better to use something like XGboost
teenaxta t1_iz9t4k2 wrote
Reply to [D] Simple Questions Thread by AutoModerator
how much of an improvement is RTX 3090 over RTX 3080 10GB for deep learning. Will be working mostly with resnet-50 or something like that
teenaxta t1_iwyr26s wrote
Reply to [P] Any object detection library by PegasusInvasion
you can try YOLO
teenaxta t1_j8qvnx0 wrote
Reply to [Discussion] The need for noise in stable diffusion by AdministrationOk2735
I think this has more to do with probability, the sum of all random variables approaches a gaussian distribution. We can prove it using Central limit theorem. So what that really means is that the noise can map all sorts of information. Also when you add noise consistently, at one point you reach the normal distribution however, the noise pattern at hand is unique. Think of it as this way, 0,0 have a mean of 0 while -1,1 also have a mean of 0. The unique noise pattern actually contains useful information where as if you were to create a blank canvas, your generator would have no idea about what to generate from it for it is a many to one mapping. The additive noise process is a unique mapping