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The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3] ImageNet contains more than 20,000 ...
Logistic activation function. The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear. [1]
In machine learning, a hyperparameter is a parameter, such as the learning rate or choice of optimizer, which specifies details of the learning process, hence the name hyper parameter. This is in contrast to parameters which determine the model itself. Hyperparameters can be classified as model hyperparameters, that typically cannot be inferred ...
Generative artificial intelligence ( generative AI, GenAI, [1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models, [2] often in response to prompts. [3] [4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has ...
Inference engine. In the field of artificial intelligence, an inference engine is a software component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.
Automated machine learning ( AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence -based solution to the growing ...
Recursive neural network. A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
on a given observable variable X and target variable Y; [1] A generative model can be used to "generate" random instances ( outcomes) of an observation x. [2] A discriminative model is a model of the conditional probability. P ( Y ∣ X = x ) {\displaystyle P (Y\mid X=x)} of the target Y, given an observation x.
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