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The most important aspect of any hot working process is controlling the temperature of the workpiece. 90% of the energy imparted into the workpiece is converted into heat. Therefore, if the deformation process is quick enough the temperature of the workpiece should rise, however, this does not usually happen in practice.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Rotary piercing is a hot working metalworking process for forming thick-walled seamless tubing. There are two types: the Mannesmann process, invented in the 1880s, and the Stiefel process, developed two decades later. [1]
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. 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.
Support-Vector Clustering [5] and other kernel methods [6] and unsupervised machine learning methods become widespread. [7] 2010s: Deep learning becomes feasible, which leads to machine learning becoming integral to many widely used software services and applications. Deep learning spurs huge advances in vision and text processing. 2020s
Environment for DeveLoping KDD-Applications Supported by Index-Structures is a similar project to Weka with a focus on cluster analysis, i.e., unsupervised methods. H2O.ai is an open-source data science and machine learning platform; KNIME is a machine learning and data mining software implemented in Java.