bearzoo

6 ms·

1. 31.▲
   by bearzoo 11y ago

   I am afraid this author has no idea what he is doing - and is loosely throwing around terms he does not understand. What the hell was his normalization procedure. Dangerous to readers who do not know a lot and will get confused while readin
2. 32.▲
   by bearzoo 11y ago

   They are doing nothing but starting with random noise, and then learning a representation of an image that will maximize the probability in the output layer (by suggesting to the network that this noise should have actually been recognized
3. 33.▲
   by bearzoo 12y ago

   ^agreed - no ANN can be treated as a 'jack of all trades' because of the success found in one domain (it seems more networks are designed to boost performance on one type of data)
4. 34.▲
   by bearzoo 12y ago

   'Deep learning' is the new 'big data'
5. 35.▲
   by bearzoo 12y ago

   From reading the abstract it seems that they are claiming that introducing some randomness into your gradient of weight changes allows for the quicker convergence of solution - I did not read the paper. I also don't exactly understand
6. 36.▲
   by bearzoo 12y ago

   The brain has no objective function it is trying to minimize
7. 37.▲
   by bearzoo 12y ago

   oh the power of buzzwords