09-29-2017
Quote:
Originally Posted by
rbatte1
I have just been on RedHat SA 3 training course (4 days) and sat exams
EX200 (RHCSA) and
EX300 (RHCE)
The daft thing was that politics meant I wasn't allowed to take courses SA 1 or 2. So I learnt about stuff I would never use (SELinux; iSCSI; NFS Kerberos encrypted with user specific access rules etc.) and then took the exams.
Somehow I passed RHCSA for the course I didn't do, even though I didn't know a few big chunks so would score zero for those sections. Sadly I failed on RHCE because there's too much to remember about irrelevant stuff that I would never dream of using and it all seems a bit over contrived.
Given that the exams marks if you can pass the exam rather than how you perform in a real job, what do people think of it's value? It might look good to management and help get an interview, but as someone not looking to move, ........ ?
Just wondering,
Robin
Hello rbatte1,
I NEVER discourage anyone from doing learning(by any good way). Personally I believe in this:
Quote:
I believe in knowledgefication(may be not a word in dictionary but self-explanatory) NOT certification.
Thanks,
R. Singh
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LEARN ABOUT PHP
fann_cascadetrain_on_data
FANN_CASCADETRAIN_ON_DATA(3) 1 FANN_CASCADETRAIN_ON_DATA(3)
fann_cascadetrain_on_data - Trains on an entire dataset, for a period of time using the Cascade2 training algorithm
SYNOPSIS
bool fann_cascadetrain_on_data (resource $ann, resource $data, int $max_neurons, int $neurons_between_reports, float $desired_error)
DESCRIPTION
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE(3) value should change
within fann_get_cascade_output_stagnation_epochs(3) during training of the output connections, in order for the training not to stagnate.
If the training stagnates, the training of the output connections will be ended and new candidates will be prepared.
This training uses the parameters set using the fann_set_cascade_..., but it also uses another training algorithm as it's internal train-
ing algorithm. This algorithm can be set to either FANN_TRAIN_RPROP or FANN_TRAIN_QUICKPROP by fann_set_training_algorithm(3), and the
parameters set for these training algorithms will also affect the cascade training.
PARAMETERS
o $ann
-Neural network resource.
o $data
-Neural network training data resource.
o $max_neurons
- The maximum number of neurons to be added to neural network.
o $neurons_between_reports
- The number of neurons between printing a status report. A value of zero means no reports should be printed.
o $desired_error
- The desired fann_get_MSE(3) or fann_get_bit_fail(3), depending on which stop function is chosen by fann_set_train_stop_func-
tion(3)
RETURN VALUES
Returns TRUE on success, or FALSE otherwise.
SEE ALSO
fann_train_on_data(3), fann_cascadetrain_on_file(3).
PHP Documentation Group FANN_CASCADETRAIN_ON_DATA(3)