Ciro Santilli @cirosantilli 35

Test buy 2023-04-10 in the UK:

Sending 5 pounds to wallet 12dg2FaiZLp3VzDtLvwPinaKz41TQcEGbs

This worked and I received 21939 satoshis (23928 - 1989) on Electrum on one of the outputs of transaction 1177268091cbeaacbcaac5dc4f6d1774c4ec11b4bcffafa555cd2775eafb954c.

Sending 1 satoshi back! The lowest fee in Electron is 1120 Satoshis targeting 25 blocks (4 hours). Let's do it. Failed, server forbids dust, minimum is 1000 satoshi. OK, sending 1000 satoshi, at 1139 fee.

CNN convolution kernels are not hardcoded. They are learnt and optimized via backpropagation. You just specify their size! Example in PyTorch you'd do just:as used for example at: activatedgeek/LeNet-5.

This can also be inferred from: stackoverflow.com/questions/55594969/how-to-visualise-filters-in-a-cnn-with-pytorch where we see that the kernels are not perfectly regular as you'd expected from something hand coded.

Object detection model.

You can get some really sweet pre-trained versions of this, typically trained on the COCO dataset.

This is about transactions that are interesting not because of their inscriptions, but for some other reason, such as transaction size, etc.

Related:

mlcommons.org/en/ Their homepage is not amazingly organized, but it does the job.

Benchmark focused on deep learning. It has two parts:Furthermore, a specific network model is specified for each benchmark in the closed category: so it goes beyond just specifying the dataset.

Results can be seen e.g. at:

And there are also separate repositories for each:

E.g. on mlcommons.org/en/training-normal-21/ we can see what the the benchmarks are:

The most important thing this project provides appears to be the .onnx file format, which represents ANN models, pre-trained or not.

Deep learning frameworks can then output such .onnx files for interchangeability and serialization.

Some examples:

Some interesting analysis by Parth Shukla twitter.com/pparth | www.linkedin.com/in/parth-shukla-59583b20/:Apparently most of the routers were Chinese. No surprise there.

Data format overview: opendata.stackexchange.com/questions/1951/dataset-of-domain-names/21077#21077

TODO was this data also obtained illegally like the Carna botnet

The student organized bar of the École. There's a corresponding Binet that takes care of it.