Machine_learning_derivation

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machine_learning_derivation is an Open Source Research & Education tool that provides notes and derivations for machine learning algorithms. It serves as a valuable resource for understanding complex ML concepts.

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Pricing

Free · Open Source

Free tier

Yes

API

Skill level

Technical

About

What is machine_learning_derivation?

machine_learning_derivation is an open-source GitHub repository offering comprehensive notes and derivations for various machine learning algorithms. It covers fundamental topics such as linear regression, support vector machines, dimensionality reduction, and probabilistic graphical models, including EM algorithm and Gaussian Mixture Models. The repository also delves into advanced concepts like variational inference, Markov Chain Monte Carlo sampling, and Kalman filtering. It is designed to support learning and research in machine learning, providing detailed explanations and mathematical derivations for each algorithm. The content is presented in PDF format, making it accessible for in-depth study and reference.

Best used for

Ideal for professors who need to access detailed mathematical derivations of machine learning algorithms, supplement their course materials, and provide in-depth explanations to students. Especially valuable for those teaching advanced machine learning courses or conducting research.

Common actions

learn machine learning

understand algorithms

access derivations

study ML concepts

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Capabilities

Key features

Algorithm derivation notes
Machine learning topics
PDF format
Mathematical explanations

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Free · Open Source

Free

FAQs

What machine learning topics are covered in the repository?

The repository covers a wide range of machine learning topics, including linear regression, linear classification, dimensionality reduction, support vector machines, kernel methods, exponential family distributions, probabilistic graphical models, EM algorithm, Gaussian mixture models, variational inference, Markov Chain Monte Carlo sampling, Kalman filtering, and conditional random fields.

In what format are the machine learning notes provided?

The notes and derivations for the machine learning algorithms are provided in PDF format. This allows for easy viewing, downloading, and printing, making them convenient for study and reference.

Is there a corresponding video course for these notes?

Yes, the README.md file in the repository links to a Bilibili course, suggesting that these notes are intended to accompany a video lecture series. This provides a multi-modal learning experience for users.

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