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The Math Of Machine Learning

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10 Best Machine Learning Algorithms (2022) - Unite.AI

10 Best Machine Learning Algorithms (2022) - Unite.AI

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Machine Learning Algorithms: The Premise

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...

1. Transformers

In 2017 Google Research led a research collaboration culminating in the paper Attention Is All You Need. The work outlined a novel architecture that promoted attention mechanisms from ‘piping’ in encoder/decoder and recurrent network models to a central transformational technology in their own ri...

The Usage Of Transformers

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...

2: Generative Adversarial Networks (GANs)

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...

The Usage Of GANs

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...

3: Support Vector Machine(SVM)

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.

...

4: K-Means Clustering

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...

5: Random Forest

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...

6: Naive Bayes

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...

7: K- Nearest Neighbors (KNN)

K-Nearest Neighbors (KNN) is a lean algorithm that still features prominently across academic papers and private sector machine learning research initiatives.

KNN has been called ‘the lazy learner’, since it exhaustively scans a dataset in order to evaluate the relationships between data po...

8: Markov Decision Process (MDP)

 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 ...

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...

10: Stochastic Gradient Descent

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...

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