Standarisasi

What we’re going to suggest is that American intellectuals – we areusing the term ‘American’ as it was used at the time, to refer toindigenous inhabitants of the Western Hemisphere; and ‘intellectual’to refer to anyone in the habit of arguing about abstract ideas –actually played a role in this conceptual revolution.

Because the policies are standardized, we can now use the policy_std columns to evaluate and analyze our IAM policies without having to convert case or account for optional use of scalar values vs array values!

AWS IAM policies can have multiple representations that mean the same thing. Converting to a standard, machine-readable format makes them easier to search, analyze, join with other data and to calculate differences.

Steampipe's standardized schemas allow you to approach tagging queries with common patterns across resources and clouds.

which is the right thing to do btw

Normalize the database for this case if your data is going to be modified multiple times

Duplicated data is a common practice when working with non-relational databases as Firebase. It saves us from performing extra queries to get data making data retrieval faster and easier

normalizing our dabatase will help us. What means normalize? Well, it simply means to separate our information as much as we can

Denormalization is a database optimization technique in which we add redundant data to one or more tables

in clustering analyses, standardization may be especially crucial in order to compare similarities between features based on certain distance measures. Another prominent example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance

As a rule of thumb I’d say: When in doubt, just standardize the data, it shouldn’t hurt.

many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they’ll have the properties of a standard normal distribution with μ=0μ=0\mu = 0 and σ=1σ=1\sigma = 1 where μμ\mu is the mean (average) and σσ\sigma is the standard deviation from the mean

Mean-field Analysis of Batch Normalization

One of the challenges of deep learning is that the gradients with respect to the weights in one layerare highly dependent on the outputs of the neurons in the previous layer especially if these outputschange in a highly correlated way. Batch normalization [Ioffe and Szegedy, 2015] was proposedto reduce such undesirable “covariate shift”. The method normalizes the summed inputs to eachhidden unit over the training cases. Specifically, for theithsummed input in thelthlayer, the batchnormalization method rescales the summed inputs according to their variances under the distributionof the data

LocalNorm: Robust Image Classification through Dynamically Regularized Normalization

Fixup Initialization: Residual Learning Without Normalization

Generalized Batch Normalization: Towards Accelerating Deep Neural Networks

How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)