1. Transformers

A naive Bayes classifier is a powerful but relatively lightweight algorithm capable of estimating probabilities based on the calculated features of data.

This level of academic and investigative rigour would be overkill where ‘common sense’ is available but is a valuable standard when trave...

K-Means Clustering has become the most popular implementation of this approach, shepherding data points into distinctive ‘K Groups’, which may indicate demographic sectors, online communities, or any other possible secret aggregation waiting to be discovered in raw statistical data.

Outside...

Beside their (actually fairly limited) involvement in popular deepfake videos, image/video-centric GANs have proliferated over the last four years, enthralling researchers and the public alike. Keeping up with the dizzying rate and frequency of new releases is a challenge, though the GitHub repos...

Transformers captured the public imagination in 2020 with the release of OpenAI’s GPT-3, which boasted a then record-breaking 175 billion parameters. This apparently staggering achieveme...

Though we’re living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms that are decades, in certain cases 70 years old.

Given the extent to which these older algorithms support and are en...

Though transformers have gained extraordinary media coverage through the release and adoption of GPT-3, the Generative Adversarial Network (GAN) has become a recognizable brand in its own right, and may eventually join deepfake as a verb.

The Generator cycles through thousa...

Term Frequency (TF) divides the number of times a word appears in a document by the total number of words in that document. Thus the word seal appearing once in a thousand-word article has a term frequency of 0.001. By itself, TF is largely useless as an indicator of term importance, due...

 The Markov Decision Process (MDP) is one of the most basic blocks of reinforcement learning architectures. A conceptual algorithm in its own right, it has been adapted into a great number of other algorithms, and recurs frequently in the current crop of AI/ML research.

MDP explores a data ...

Stochastic Gradient Descent (SGD) is an increasingly popular method for optimizing the training of machine learning models.

Gradient Descent itself is a method of optimizing and subsequently quantifying the improvement that a model is making during training.

SGD has become the most po...

Random Forest is an ensemble learning method that averages the result from an array of decision trees to establish an overall prediction for the outcome.

As with many of the algorithms in this list, Random Forest typically operates as an ‘early’ sorter and filter of data, and as such consis...

Originated in 1963, Support Vector Machine (SVM) is a core algorithm that crops up frequently in new research. Under SVM, vectors map the relative disposition of data points in a dataset, while support vectors delineate the boundaries between different groups, features, or traits.

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