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
A Random Variable is defined as a variable whose possible values are outcomes of a random phenomenon.| DeepAI
The normal distribution is the most important and most widely used distribution in statistics. It is sometimes called the bell curve or Gaussian distribution, because it has a peculiar shape of a bell. Mostly, a binomial distribution is similar to normal distribution. The difference between the two is normal distribution is continuous.| 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
Probability in deep learning is used to mimic human common sense by allowing a machine to interpret phenomena that it has no frame of reference for.| DeepAI
A field of computer science that aims to teach computers how to learn and act without being explicitly programmed.| DeepAI
Bayes’ theorem is a formula that governs how to assign a subjective degree of belief to a hypothesis and rationally update that probability with new evidence. Mathematically, it's the the likelihood of event B occurring given that A is true.| DeepAI
