Outlier Definition | DeepAI
An outlier is any datapoint in a dataset beyond a pre-defined range, usually representing a measurement error or abnormal data that should not be included.| DeepAI
An outlier is any datapoint in a dataset beyond a pre-defined range, usually representing a measurement error or abnormal data that should not be included.| DeepAI
A moment is a quantitative measurement for the shape of a function. Moments are applied in both mechanics and mathematics as ways of describing sample distributions.| DeepAI
Geometric distribution is a probability distribution that represents the number of trials needed to obtain a success in a Bernoulli experiment.| DeepAI
The Gamma distribution is a family of right-skewed, continuous probability distributions used in statistics and probability theory.| DeepAI
A cumulative distribution function (CDF) describes the cumulative probability of any given function below, above or between two points.| DeepAI
Game Theory is the study of micro-situations where each situation demands a decision that| DeepAI
In machine learning, an estimator is an equation for picking the “best,” or most likely accurate, data model based upon observations in realty.| DeepAI
Artificially intelligent tools for naturally creative humans.| DeepAI
A synapse is the connection between nodes, or neurons, in an artificial neural network (ANN).| DeepAI
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
In short, variance is the measurement of the distance of a set of random numbers from their collective average value.| DeepAI
Skewness is a quantifiable measure of how distorted a data sample is from the normal distribution.| DeepAI
A Random Variable is defined as a variable whose possible values are outcomes of a random phenomenon.| DeepAI
A Probability Density Function is a statistical expression used in probability theory as a way of representing the range of possible values of a continuous random variable. The area under the curve represents the interval of which a continuous random variable will fall, and the total area of the interval represents the probability that the variable will occur.| 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
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
The exponential distribution, also known as the negative exponential distribution, is a probability distribution that describes time between events in a Poisson process.| 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
Backpropagation, short for backward propagation of errors, is a widely used method for calculating derivatives inside deep feedforward neural networks.| DeepAI
Probability Theory describes probabilities in terms of a probability space, typically assigning a value between 0 and 1, known as the probability measure, and a set of outcomes known as the sample space.| DeepAI
In machine learning and artificial intelligence, Supervised Learning refers to a class of systems and algorithms that determine a predictive model using data points with known outcomes.| 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
An artificial neural network learning algorithm, or neural network, or just neural net, is a computational learning system that uses a network of functions to understand and translate a data input of one form into a desired output, usually in another form.| DeepAI
In simple words, Natural Language Processing is a field which aims to make computer systems understand human speech. NLP is comprised of techniques to process, structure, categorize raw text and extract information.| DeepAI
Deep learning is a machine learning method using multiple layers of nonlinear processing units to extract features from data. Find out more on DeepAI.| DeepAI
A decision tree is a supervised learning technique that has a pre-defined target variable and is most often used in classification problems.| DeepAI
The application of rapid data processing, machine learning, predictive analysis, and automation to simulate intelligent behavior and problem solving capabilities with machines and software.| 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
Pattern recognition is a technique to classify input data into classes or objects by recognizing patterns or feature similarities.| DeepAI
A classifier is any deep learning algorithm that sorts unlabeled data into labeled classes, or categories of information.| DeepAI
Binarization is the process of transforming data features of any entity into vectors of binary numbers to make classifier algorithms more efficient.| DeepAI
Bayesian inference refers to the application of Bayes’ Theorem in determining the updated probability of a hypothesis given new information.| DeepAI
