Normal

Normal extension (or quasi-Galois) body extension that divides the set of polynomials on the basis

A projection variable embedded by a complete linear system like regular change, reasonable normal scrolling (not related to the concept of the above-mentioned normal scheme)

Normal number (calculation), floating point number within the equilibrium range supported by a specific format (not related to the previous concept)

Normal coordinates, differential geometry, local coordinates obtained from an exponential graph (Riemannian geometry)

Normalized image input: Data normalization is an important step in ensuring that each input parameter (in this case, pixels) has a similar data distribution. This makes convergence faster when training the network. Data normalization is done by subtracting the average value from each pixel and dividing the result by the standard deviation. The distribution of this data is similar to zero center Gaussian curve. Since you need a positive number of pixels for image input you may choose to normalize the data within the zoom range. In the data set example, the following montage represents normalized data

Strictly speaking, speaking of 'normal distribution' is not true as there are many normal distributions. The mean and standard deviation of the normal distribution may be different. Figure 1 shows three normal distributions. The average value of the green (leftmost) distribution is -3, the standard deviation is 0.5, the average of red distribution (central distribution) is 0, the standard deviation is 1, and the average of black distribution (rightmost) It is 2. The standard deviation is 3. All of these and all other normal distributions are symmetrical, there are relatively many values ​​at the center of the distribution and relatively few values ​​at the end.

For all normal or nearly normal distributions, there is a constant ratio between the area under the curve and the average when measured in standard deviation units, there is a constant ratio between arbitrary distances from the average . For example, with all normal curves, 99.73% of cases fall within 3 standard deviations from the mean, 95.45% cases fall within 2 standard deviations from the average, and 68.27% case fits. It falls within the mean range of the standard deviation. Although the normal distribution is theoretical, some of the variables studied by researchers are very similar to normal curves. For example, standardized test scores (eg SAT, ACT, and GRE) are generally similar to normal distributions. Height, athletic ability, and many social and political attitudes of people in a particular group often resemble Bell curves.