Training algorithms are a class of algorithms used in machine learning to tune the parameters of a machine learning model from a training data set. The goal is for the model to be able to generalise and make accurate predictions about new data that has not been seen before.
Training algorithms can be supervised, unsupervised or boosting. Supervised training algorithms use a labelled dataset to learn to map features to known labels. Unsupervised training algorithms are used to learn patterns in an unlabelled dataset. Finally, reinforcement training algorithms are used to learn to make decisions based on feedback from the environment.
Examples of training algorithms include stochastic gradient descent (SGD), the backpropagation algorithm and the k-means clustering algorithm. Stochastic gradient descent is one of the most widely used algorithms in machine learning to adjust the parameters of a machine learning model. The backpropagation algorithm is used to train artificial neural networks. The k-means clustering algorithm is used to group data into different clusters.
Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
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