In the EU we have the incoming GDPR to legislate for (and penalise) data breaches like this. This directive is very clear and detailed on how data should be collected, securely stored and disposed of. US law is a decade behind the EU.
Readit Newsdandermotj commented on Computer Vision by Andrew Ng – Lessons Learned kdnuggets.com/2017/12/ng-... · Posted by u/jonbaer
Where do you get pretrained networks from? I looked for awhile but wasn't able to find any
Like if I wanted one of these: https://github.com/hwalsuklee/tensorflow-generative-model-co...
Googling LeNet, Reset, AlexNet or other well known architectures will provide you with links to the network weights.
dandermotj commented on Introduction to R Programming cecilialee.github.io/blog... · Posted by u/cecilialee
I'm always a bit disappointed that yhat indiscriminately used 'ggplot' for the python package name. Using a variation on the name would have been more considerate.
Serious question: how does one evaluate the results reproducibility of this paper ?
Maybe I'm missing some things but:
- Are 1st gen TPUs even accessible ? You have to fill out a form to learn more about those second generation TPUs: https://cloud.google.com/tpu/
- I can't find the source code
This does not look like a scientific paper, but a (very impressive) tech demo.
This is definitely a scientific paper. Pretty much no scientific paper comes with source code and the majority of scientific papers are not reproducible without an entire university department of resources anyway.
The French were primary architects of the EU. It's a bit rich to complain now that Ireland and Apple are taking advantage of its rules.
As an Irish and European person, I want you to know that your comment is misinformed and misleading. Global tax avoidance and evasion by multinationals has no relation to the formation of the EU, and the Irish government are at a gigantic economic loss (not advantage) as a result.
Complying with the letter of the law. Not the spirit.
Breaking the spirit of the law, but not the letter.
Deleted Comment
Not a statistician, but this still seems flawed. The pretend votes need to be related to the person seeing the list of items. These normally come from the population (i.e. if you were ranking Netflix, the pretend votes would be the sum of all votes that exist for every movie, grouped by star count). This makes sense, because if you had no other information, your guess would just be the average of all the existing ratings.
The problem is that the pretend votes need to be culled in order to be predictive. Otherwise they dominate in the arithmetic. They need to be more specific to the user looking at the ranking. Continuing with the Netflix example, if a user was looking for scary movies, the pretend votes need to come from the corpus of all scary movies, rather than all movies that exist.
Here's the problem, there doesn't seem to be a good way to narrow the pretend votes. Worse, there isn't a good way to combine the two. If the pretend votes came from two sources, its not clear what to do. For example, if the user is from California, the California pretend votes (priors?) need to be combined with the scary movie pretend votes.
How can we add pretend votes without justifying where they came from?
I like to think about it this way. By not explicitly imposing a prior, you are implicitly imposing a prior that each item will receive no votes. This is totally non sensical because of course these items will get votes.
Just because we don't know what the true value of p will be doesn't mean we don't have some expectation. If I asked you what you expect the popularity of a given item will be, you won't say 0, you'll say something like the average. So why assume all items will have 0 votes in our model?
So then let's have permanent war because all that spending on bombs is economically beneficial to everyone, right?
Say hello to the military-industrial complex
[1] https://dandermotj.github.io/post/review-deeplearning-ai-cou...