Category : 7n47a00129
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System Under Test. Set: 'sut'. Boot Firmware Settings. Management Firmware Settings. System Under Test Notes. Using the local security settings console, "lock pages in memory" was enabled for the user running the benchmark. Run was started on controller using Remote Desktop.
Controller System. Measurement Devices. Aggregate Electrical and Environmental Data. Aggregate Performance Data.
See the Host Performance Report for additional details. Test Sponsor:. SPEC License :. Test Method:. Tested By:. Test Location:. Test Date:. Hardware Availability:. Software Availability:. System Source:.
System Designation:. Power Provisioning:. Active Idle. Set Identifier:.
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You may also like.This is the date and time in which the deepnet was updated with microsecond precision. A number between 0 and 1 specifying the rate at which to drop weights during training to control overfitting.
A dictionary with an entry per field in the dataset used to build the deepnet. Whether alternate layers should learn a representation of the residuals for a given layer rather than the layer itself or not. Complete information of the network. The key is the name of the algorithm used. Whether to learn a tree-based representation of the data as engineered features along with the raw features, essentially by learning trees over slices of the input space and a small amount of the training data.
Each layer is a map, and its structure will vary depending on the structure of the layers. This includes per-node class names for classification problems and distribution information of the objective for regression problems. A list of maps, each one of which is a preprocessor, specifying one input feature to the network.
This layer may comprise binary encoding, normalization, and feature selection, as there may be less preprocessors than features in the original data. A status code that reflects the status of the deepnet creation. Number of milliseconds that BigML took to process the deepnet. Example: 1 combiner optional Specifies the method that should be used to combine predictions in a non-boosted ensemble.
For classification ensembles, the combination is made by majority vote. The options are: 0: plurality weights each model's prediction as one vote. You can set up both using the threshold argument.
If there are less than k models voting class, the most frequent of the remaining categories is chosen, as in a plurality combination after removing the models that were voting for class.
The confidence of the prediction is computed as that of a plurality vote, excluding votes for the majority class when it's not selected. For regression ensembles, the predicted values are averaged. For a logistic regression, input data for all numerical fields except the objective field must be provided. Example: "my new prediction" private optional Whether you want your prediction to be private or not.
This will be 201 upon successful creation of the prediction and 200 afterwards. Make sure that you check the code that comes with the status attribute to make sure that the prediction creation has been completed without errors.How to get started with BET365. Follow these steps: Go on the Bet365 website by clicking the button below.
LENOVO LEN#7N47A00129 M.2 CV1 32GB SATA 6Gbps NonSSD
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Lenovo ThinkSystem CV1 - solid state drive - 32 GB - SATA 6Gb/s
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I don't know so much about poker, but I figured it was worth trying out as I like card games in general.The Best Smart Glasses of 2020
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