Artificially intelligent tools for naturally creative humans.| 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
A Uniform Distribution is a distribution in which there equal probabilities across all the values in the set.| DeepAI
Standard deviation is the measure of dispersion, or how spread out values are, in a dataset.| 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
A Poisson Distribution is a statistical distribution used to express the probability of a given number of events occurring within a fixed interval of time or space.| 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
A vector is a data structure with at least two components, as opposed to a scalar, which has just one. For example, a vector can represent velocity, an idea that combines speed and direction: wind velocity = (50mph, 35 degrees North East). A scalar, on the other hand, can represent something with one value like temperature or height: 50 degrees Celsius, 180 centimeters. Therefore, we can represent two-dimensional vectors as arrows on an x-y graph, with the coordinates x and y each representin...| DeepAI
Open source is the terminology used to describe products in which the source code, design documents, and content are all available for free. While the term was originally used exclusively in software, it has since expanded to cover other open content forms of collaboration.| DeepAI
Feature extraction is a process by which an initial set of data is reduced by identifying key features of the data for machine learning.| DeepAI
Backpropagation, short for backward propagation of errors, is a widely used method for calculating derivatives inside deep feedforward neural networks.| DeepAI
Dummy or Boolean variables are qualitative variables that can only take the value 0 or 1 to indicate the absence or presence of a specified condition.| DeepAI
This is an AI Image Generator. It creates an image from scratch from a text description.| DeepAI
The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.| 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
