https://github.com/aws-samples/aws-mlu-explain

Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/
https://github.com/aws-samples/aws-mlu-explain

ai aws d3 datavisualization dataviz deep-learning machine-learning machinelearning mlu svelte

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Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/

• Host: GitHub
• URL: https://github.com/aws-samples/aws-mlu-explain
• Owner: aws-samples
• License: other
• Created: 2021-10-13T22:58:27.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-11-12T14:13:22.000Z (about 2 months ago)
• Last Synced: 2024-12-21T13:02:09.837Z (10 days ago)
• Topics: ai, aws, d3, datavisualization, dataviz, deep-learning, machine-learning, machinelearning, mlu, svelte
• Language: JavaScript
• Homepage: https://mlu-explain.github.io/
• Size: 64.2 MB
• Stars: 632
• Watchers: 20
• Forks: 87
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
![MLU-Explain Logo & Title](./assets/readme_header.png)

This repository holds the code used for Amazon's [MLU-Explain](https://mlu-explain.github.io/) educational articles on machine learning. MLU-Explain exists to illustrate core machine learning concepts using visual essays in a fun, informative, and accessible manner.

This material exists as supplementary educational material for [Machine Learning University (MLU)](https://aws.amazon.com/machine-learning/mlu/), which provides anybody, anywhere, at any time access to the same machine learning courses used to train Amazon’s own developers on machine learning.

# Articles

## Linear Regression



**Title**: [Linear Regression](https://mlu-explain.github.io/linear-regression/)

**Summary**: A visual, interactive explanation of linear regression for machine learning.

**Code**: [/code/linear-regression/](/code/linear-regression)

**Authors**: Jared Wilber

## Logistic Regression



**Title**: [Logistic Regression](https://mlu-explain.github.io/logistic-regression/)

**Summary**: Learn about how logistic regression can be used for binary classification through an interactive example.

**Code**: [/code/logistic-regression/](/code/logistic-regression)

**Authors**: Erin Bugbee, Jared Wilber

## ROC & AUC



**Title**: [ROC & AUC](https://mlu-explain.github.io/roc-auc/)

**Summary**: A visual explanation of the Receiver Operating Characteristic Curve (ROC) curve, how it works with a live interactive example, and how it relates to Area Under The Curve (AUC).

**Code**: [/code/roc-auc/](/code/roc-auc)

**Authors**: Jared Wilber

## Train, Test, And Validation Sets



**Title**: [Train, Test, and Validation Sets](https://mlu-explain.github.io/train-test-validation/)

**Summary**: Learn why it is best practice to split your data into training, testing, and validation sets, and explore the utility of each with a live machine learning model.

**Code**: [/code/train-test-validation/](/code/train-test-validation)

**Authors**: Jared Wilber, Brent Werness

## Precision & Recall



**Title**: [Precision & Recall](https://mlu-explain.github.io/precision-recall/)

**Summary**: When it comes to evaluating classification models, accuracy is often a poor metric. This article covers two common alternatives, Precision and Recall, as well as the F1-score and Confusion Matrices.

**Code**: [/code/precision-recall/](/code/precision-recall)

**Authors**: Jared Wilber

## Random Forest



**Title**: [Random Forest](https://mlu-explain.github.io/random-forest/)

**Summary**: Learn how the majority vote and well-placed randomness can extend the decision tree model to one of machine learning's most widely-used algorithms, the Random Forest.

**Code**: [/code/random-forest/](/code/random-forest)

**Authors**: Jenny Yeon, Jared Wilber

## Decision Trees



**Title**: [Decision Trees](https://mlu-explain.github.io/decision-tree/)

**Summary**: Explore one of machine learning's most popular supervised algorithms: the Decision Tree. Learn how the tree makes its splits, the concepts of Entropy and Information Gain, and why going too deep is problematic.

**Code**: [/code/decision-tree/](/code/decision-tree)

**Authors**: Jared Wilber, Lucía Santamaría

## Bias Variance Tradeoff



**Title**: [The Bias Variance Tradeoff](https://mlu-explain.github.io/bias-variance/)

**Summary**: Understand the tradeoff between under- and over-fitting models, how it relates to bias and variance, and explore interactive examples related to LASSO and KNN.

**Code**: [/code/bias-variance/](/code/bias-variance)

**Authors**: Jared Wilber, Brent Werness

## Double Descent: A Visual Introduction



**Title**: [Double Descent](https://mlu-explain.github.io/double-descent/)

**Summary**: Meet the double descent phenomenon in modern machine learning: what it is, how it relates to the bias-variance tradeoff, the importance of the interpolation regime, and a theory of what lies behind.

**Code**: [/code/double-descent/](/code/double-descent)

**Authors**: Jared Wilber, Brent Werness

## Double Descent 2: A Mathematical Explanation



**Title**: [Double Descent 2](https://mlu-explain.github.io/double-descent2/)

**Summary**: Deepen your understanding of the double descent phenomenon. The article builds on the cubic spline example introduced in Double Descent 1, describing in mathematical detail what is happening.

**Code**: [/code/double-descent2/](/code/double-descent2)

**Authors**: Brent Werness, Jared Wilber

## Running Locally

This article holds code for each articles, as well as the generated builds from the code (e.g. the static assets comprising the articles).

First, clone this repo.

```bash

git clone https://github.com/aws-samples/aws-mlu-explain.git

```

Next, cd into the article of interest and install the required libraries.

```bash

# e.g. bias variance tradeoff article

cd bias-variance

# install libraries

npm install

```

Now, to run the development version:

```bash

npm start

```

To build and view the static assests:

```bash

# build assets

npm run build

# view generated article

cd dist/

# run local server

python3 -m http.server # or just `live-server`

```

## License Summary

This open source articles are made available under the Creative Commons Attribution-ShareAlike 4.0 International License. See [LICENSE](LICENSE) file.

The sample and reference code within this open source book is made available under a modified MIT license. See the [LICENSE-SAMPLECODE](LICENSE-SAMPLECODE) file.