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There are many terms used in the field of machine learning, but some of the most frequently used ones include:
A mathematical representation of a system or phenomenon that can be used to make predictions or decisions.
The process of using a labeled dataset to “train” a machine learning model to make accurate predictions or decisions.
A set of data is used to evaluate the performance of a trained machine-learning model.
A measurable property or characteristic of a data sample that is used as an input to a machine learning model.
A type of machine learning where the model is trained on labeled data, and the goal is to predict the label for new data.
A type of machine learning where the model is trained on unlabeled data, and the goal is to find patterns or structures in the data.
A subfield of machine learning that involves training artificial neural networks with multiple layers to learn representations of data.
An optimization algorithm is used to find the values of the parameters of a model that minimize a loss function.
A function that measures the difference between the predicted values and the true values of a model.
A phenomenon occurs when a model is trained too well on the training data and performs poorly on new data.
Techniques used to prevent overfitting by adding a penalty term to the loss function.
Parameters that are not learned from data, but are set before training a model, examples are the learning rate and the number of hidden layers in a neural network.
A term used to measure the frequency of passing the whole training dataset through the model.
This is not an exhaustive list, and there are many more terms used in machine learning, but these are some of the most fundamental and commonly used ones.
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