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Because log(x) is the sum of the terms of the form log(1 + 2 −k) corresponding to those k for which the factor 1 + 2 −k was included in the product P, log(x) may be computed by simple addition, using a table of log(1 + 2 −k) for all k. Any base may be used for the logarithm table. [53]
Decimal degrees (DD) is a notation for expressing latitude and longitude geographic coordinates as decimal fractions of a degree.DD are used in many geographic information systems (GIS), web mapping applications such as OpenStreetMap, and GPS devices.
A repeating decimal or recurring decimal is a decimal representation of a number whose digits are eventually periodic (that is, after some place, the same sequence of digits is repeated forever); if this sequence consists only of zeros (that is if there is only a finite number of nonzero digits), the decimal is said to be terminating, and is not considered as repeating.
log-log folded and scales, for working with logarithms of any base and arbitrary exponents. 4, 6, or 8 scales of this type are commonly seen. Ln linear scale used along with the C and D scales for finding natural (base e {\displaystyle e} ) logarithms and e x {\displaystyle e^{x}}
Nano (symbol n) is a unit prefix meaning one billionth.Used primarily with the metric system, this prefix denotes a factor of 10 −9 or 0.000 000 001.It is frequently encountered in science and electronics for prefixing units of time and length.
The log-normal distribution has also been associated with other names, such as McAlister, Gibrat and Cobb–Douglas. [4] A log-normal process is the statistical realization of the multiplicative product of many independent random variables, each of which is positive.
Eliminate ambiguous or non-significant zeros by using Scientific Notation: For example, 1300 with three significant figures becomes 1.30 × 10 3. Likewise 0.0123 can be rewritten as 1.23 × 10 −2. The part of the representation that contains the significant figures (1.30 or 1.23) is known as the significand or mantissa.
While the descent direction is usually determined from the gradient of the loss function, the learning rate determines how big a step is taken in that direction. A too high learning rate will make the learning jump over minima but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum.