At some point Bishop mentions “risk”. I’ve seen this term come up elsewhere too but never really understood what it means. Does anyone here know?
Interesting stuff. I’d really like to become more familiar with LA. Can anyone recommend me a book that has some useful exercises on this stuff?
Thanks guys! I understand it better now. When would you encounter this problem in real-life applications, though? I just don't get why Bishop gives us this information, haha :)
@hdidwania I understand. Thanks. His point would then be that we can't simply optimize a change-of-variables function if the transformation is non-linear? Still find it hard to see the practical value of this information :) But it makes more sense, thanks!
@ jdry1729 I think the 1-star exercises are a good start? Or would those be too many? Personally, I've already done the first few chapters' exercises so I don't really mind, but I think it is crucial to discuss them to gain a thorough understanding.
I have a few questions:
About your second point: an approach to neurons with greater natural realism can be found in Spiking Neural Networks. Natural neurons fire after they have received a stimulus that built up until their 'potential' reached a certain threshold. The problem with replicating these neurons artificially is that this function isn't differentiable. However, there have been some successes recently. For a paper: see https://arxiv.org/pdf/1804.08150.pdf .
Definitely interested. Two other guys and I just started a discord channel for PMRL, too. Do you want to merge, i.e. the sharing link to the channel?
Some suggestions we have: