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Artificial dataset covering 7 classes of animals. Animals are classed into 7 categories and features are given for each. 101 Text Classification 1990 [293] R. Forsyth Demospongiae Dataset Data about marine sponges. 503 sponges in the Demosponge class are described by various features. 503 Text Classification 2010 [294] E. Armengol et al.
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
By 2019, graphics processing units , often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. [159] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of ...
Artificial Intelligence Markup Language (AIML) [11] is an XML dialect [12] for use with Artificial Linguistic Internet Computer Entity (A.L.I.C.E.)-type chatterbots. Planner is a hybrid between procedural and logical languages. It gives a procedural interpretation to logical sentences where implications are interpreted with pattern-directed ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
It is the combination of automation and ML. [1] 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 challenge of applying machine learning.
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Training an autoencoder intrinsically constitutes a self-supervised process, because the output pattern needs to become an optimal reconstruction of the input pattern itself. However, in current jargon, the term 'self-supervised' often refers to tasks based on a pretext-task training setup.