Skip all that and cut to "a neural network." Presto.
I wish I understood this simplicity when I first got into ML. I was always annoyed with claims like "a NN will solve your problems." Oh really?
Yeah. Really.
You can learn the theory later. Sure, applying SVD or nonnegative matrix factorization to the user interaction matrix, truncating to get a low-rank symmetric approximation makes sense now. (I have to pause and think about it for a little bit, but I do indeed understand each part.)
The cool thing was, I didn't need to. The theory helps inform my future ML training runs. But I was able to get most of the magic without knowing a damn thing.
Literally just throw Pytorch at the problem and screw around until it starts giving results. You'll be amazed and shocked and delighted how far that gets you. (And all the professional mathematicians and statisticians will be disgusted, but who cares?)
I wish I understood this simplicity when I first got into ML. I was always annoyed with claims like "a NN will solve your problems." Oh really?
Yeah. Really.
You can learn the theory later. Sure, applying SVD or nonnegative matrix factorization to the user interaction matrix, truncating to get a low-rank symmetric approximation makes sense now. (I have to pause and think about it for a little bit, but I do indeed understand each part.)
The cool thing was, I didn't need to. The theory helps inform my future ML training runs. But I was able to get most of the magic without knowing a damn thing.
Literally just throw Pytorch at the problem and screw around until it starts giving results. You'll be amazed and shocked and delighted how far that gets you. (And all the professional mathematicians and statisticians will be disgusted, but who cares?)