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--armor specifies that base64 encoding will be used. That ensures that all characters will be unambiguous and easy to read. --output BobsPrivateKeyFile.asc specifies what name you wish to give the file, and bob@gmail.com specifies which private key on the keyring you wish to export. The...
Well, I think this is good enough for now. Keep in mind that our goal is to use the dongle and sensors on our own platform. So, let’s plugin the dongle on a Rapsberry pi device, and try to repeat these steps with some python script. Here are some additional findings when I worke...
**NOTE:** gedit3.8+, at least in Ubuntu expects python3 while earlier versions did not. For now Python plugins that have been updated may not work in version 3.0-3.6. If you have a desire for backwards compatibility create an issue. ...
Also, I've been told by an expert, that when you post an image it clutters up the thread and mysteriously, over time, the link to the image will somehow become "unstable" or something to that effect. :) I can only surmise that is due to Global Warming of the threads....
a method to exclude one or some columns in output of Get-process cmdlet A parameter cannot be found that matches parameter name A parameter cannot be found that matches parameter name 'Encoding'. A parameter cannot be found that matches parameter name 'Searchbase' A parameter cannot be found...
The class labels are integers and must be converted to a one hot encoding prior to modeling. This can be achieved using the to_categorical() Keras utility function. 1 2 3 # one hot encode target values trainY = to_categorical(trainY) testY = to_categorical(testY) We...
The class labels are integers and must be converted to a one hot encoding prior to modeling. This can be achieved using the to_categorical() Keras utility function. 1 2 3 # one hot encode target values trainY = to_categorical(trainY) testY = to_categorical(testY) ...
The side effect of having to one-hot encode the variable is that if the dataset contains high cardinality features, it can be memory intensive and computationally expensive because each category becomes its own binary column. While implementing one-hot encoding itself is not a difficult transformatio...
1: No need for one-hot encoding CatBoost handles categorical features in plaintext, avoiding the need for extensive preprocessing, as with other GBDTs that insist on numerical inputs. The most common preprocessing method for categorical data is labeling it though one-hot encoding, which breaks da...