@socrates and Scholar Social in general.
Do you know a study which looks into why people share news on social media? What's people intent?
So far, I see that people might just share a news story they find important or give their opinion/comment. On a meta level, one can comment on the quality of a news outlet. Or even call out fake news and propaganda.
Are there studies that classify shared content similarly?
I've started reading the text version of NPR https://text.npr.org Not surprisingly, I pay much more attention to the content, rather than to the visual packaging.
I've been heavily refactoring code of my machine learning project. Now I can just watch how GPUs are fully loaded, while the CPUs are not that much. Can't wait for first validation results. It's a slightly modified model which takes images of different sizes. I don't know yet wether it gives better results, but it definitely heats up the room.
The one I had was too short, now I got a cable which is long enough to reach my home.
The server crashed shortly after I left, so no great results.
vmtouch is a nice tool to cache file content into memory.
Then I relaised, that resising images on the fly might be the main reason of the slow down. This idea came to me on Friday afternoon. The end of the day was intense, but resizing images once and staving the sizes I need improved the speed. Still, the GPU is not 100% busy. Now I'm looking for Monday, to see the results!
At that point TemsorFlow was complaining that the CPU supports instructions that the binary was not built with. Before building TF from source, I decided to update cuda and other Nvidia software. After a dance following outdated tutorials, I got it done. Speed improved, but still, while CPUs were at 100%, GPUs were barely loaded.
When I moved my code to a much more powerful machine with lots of memory, cpus and gpus, I got a GPU enabled TemsorFlow binary. That speed up the things, but still CPU was a bottleneck.
Initially I worked on my laptop with an extremely small values, for example, images were resized to 128 by 128, which feels too small. However, I managed to get reasonable performance numbers.
I picked up Keras, so the code for the network itself is simple. Complexity is around: dataset preparation, evaluation and model selection.
Computer science, computational linguistics, running, swimming, photography.
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