Neural network consisting of multiple hidden convolutional and pooling layers. Excels at image classification.
CNN model with support for two-dimensional image data.
CNN model with support for three-dimensional data.
Neural network optimal for time series data.
Uses CNN layers in combination with LSTMs for sequence prediction involving spatial input. Excels at video processing.
Neural network consisting of multiple hidden layers.
The arithmetic mean of the logarithms of the absolute values of the numbers in a given set.
The arithmetic mean of all absolute values in a given set of numbers.
The peak value divided by the squared mean value of the square roots of the absolute amplitudes.
The ratio of the standard deviation to the mean. Also known as the relative standard deviation (RSD).
The ratio of the peak value to the root mean square value of a waveform. Indicates how extreme the peaks of a wave are.
The ratio of the maximum absolute value to the absolute mean of a given set of numbers.
A statistical measure of how heavily the tails of a distribution differ from the tails of a normal distribution.
The largest number in a set of numbers.
The sum of all numbers in a given set divided by the total number of data points.
The average of the absolute deviations from a central point.
In an odd number of ascending observations, the median is the middle value. In an even number of ascending observations, the median is the average of the two middle values.
The median of the absolute deviations from a central point.
The arithmetic mean of the maximum and minimum values in a dataset.
The smallest number in a set of numbers.
The difference between the maximum and minimum numbers in a set of numbers.
The square root of the mean of the squares of all numbers in a set.
The ratio of the root mean square to the absolute mean of a given set of numbers.
The statistical measure of the asymmetry of a distribution of real-valued observations about the mean.
The statistical measure of the spread of a set of values. A low standard deviation indicates the values tend to be close to the mean of the set, while a high standard deviation indicates the values are spread out over a wider range.
The statistical measure of how much a random variable differs from its expected value. Variation is the average of the squares of the differences between each observation and the expected value; in other words, variance is the square of the standard deviation.
