After 3 years residing on Google Cloud, we decided to move to AWS. GC is generally OK, however, there is some issue related to the architecture of the previous one that may hinder the future scalability! As such, we decided to re-architecture the site and its workload for the future. There may be some issues after the migration. However, we will check and fix any issue asap. Refer to the...
To remember the terms easier, we collect and provide a cheat sheet to several common VMware products. 1.1 VMWARE OVERVIEW NameSummaryVMwareHeadquartered at 3401 Hillview Avenue, Palo Alto; 117 offices worldwidevSphere SuiteVirtualizationvSANStorageNSXNetworkingvRealize SuiteManagementVCF (VMware Cloud Foundation)An integrated software stack for everything SDDC requiresReferenceWikipedia: VMware...
Sometimes a picture is worth a thousand words, and that’s where these cheat sheets come in handy. Cloud Developer Advocate Priyanka Vergadia has built a number of guides that help developers visually navigate critical decisions, whether it’s determining the best way to move to the cloud, or deciding on the best storage options. Below are five of her top cheat sheets in one handy...
One way to get a diverse set of classifiers is to use very different training algorithms,as just discussed. Another approach is to use the same training algorithm for everypredictor but to train them on different random subsets of the training set. Whensampling is performed with replacement, this method is called bagging(short for bootstrap aggregating). When sampling is performed without...
The squared hinge loss is a loss function used for “maximum margin” binary classification problems. Mathematically it is defined as: where ŷ the predicted value and y is either 1 or -1. Thus, the squared hinge loss is: 0* when the true and predicted labels are the same and* when ŷ≥ 1 (which is an indication that the classifier is sure that it’s the correct label)quadratically increasing with the...