The Law of Large Numbers is a theorem within probability theory that suggests that as a trial is repeated, and more data is gathered, the average of the results will get closer to the expected value. As the name suggests, the law only applies when a large number of observations or tests are considered.| DeepAI
Central Limit Theorem states that the distribution of observation means approaches a normal distribution model as the sample size gets larger.| DeepAI
Weight is the parameter within a neural network that transforms input data within the network's hidden layers. As an input enters the node, it gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network.| DeepAI
A Uniform Distribution is a distribution in which there equal probabilities across all the values in the set.| DeepAI
Skewness is a quantifiable measure of how distorted a data sample is from the normal distribution.| DeepAI
A Gaussian distribution, also known as a normal distribution, is a type of probability distribution used to describe complex systems with a large number of events.| DeepAI
A discrete random variable is a random variable with a limited and countable set of possible values.| DeepAI
Continuous random variables are variables with an infinite range of possible values, as opposed to discrete variables with defined ranges.| DeepAI
Binomial distribution is the sum of all successes in repeated independent trials conducted on an infinite, identical population.| DeepAI
A Probability Distribution is the sum of the probabilities of the events occurring. There are two distinct types of probability distributions, continuous and discrete.| DeepAI
