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One-hot is a technique to represent categorical data as binary vectors with only one bit set to 1. It is used in digital circuits, natural language processing, and machine learning to avoid assumptions about the order or importance of categories.
A dummy variable (or indicator variable) is a binary variable that indicates the presence or absence of a categorical effect in regression analysis. Learn how to use dummy variables to represent categorical variables, control for confounding factors, and avoid multicollinearity.
Learn about features, feature vectors, and feature engineering in machine learning and pattern recognition. Features are measurable properties of a phenomenon, feature vectors are numerical representations of objects, and feature engineering is the process of constructing and selecting features.
Feature hashing is a machine learning technique that turns arbitrary features into indices in a vector or matrix by applying hash functions. It can also be used for dimensionality reduction and has geometric properties related to kernel functions.
BERT is a large language model introduced by Google in 2018, based on the transformer encoder architecture. It learns contextual representations of text by self-supervised learning and can be fine-tuned for various natural language processing tasks.
An autoencoder is a neural network that learns efficient codings of unlabeled data by encoding and decoding it. Learn about different types of autoencoders, such as sparse, denoising and variational, and how they are used for dimensionality reduction, feature learning and generative modeling.
Feature scaling is a method to normalize the range of data features for machine learning algorithms. Learn about rescaling (min-max normalization), mean normalization, standardization, and scaling to unit length, and how they affect convergence and performance.
Learn how machine learning algorithms use different data sets to learn, tune, and evaluate their performance. Find out the definitions, examples, and strategies of training, validation, and test data sets.