https://github.com/solygambas/javascript-machine-learning

6 projects to understand basic algorithms in Machine Learning, using Lodash and Tensorflow.js.
https://github.com/solygambas/javascript-machine-learning

javascript lodash machine-learning tensorflow tensorflowjs

Last synced: 2 months ago
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6 projects to understand basic algorithms in Machine Learning, using Lodash and Tensorflow.js.

• Host: GitHub
• URL: https://github.com/solygambas/javascript-machine-learning
• Owner: solygambas
• Created: 2021-07-18T17:58:34.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2022-05-02T04:47:51.000Z (about 4 years ago)
• Last Synced: 2025-04-07T22:15:41.650Z (about 1 year ago)
• Topics: javascript, lodash, machine-learning, tensorflow, tensorflowjs
• Language: JavaScript
• Homepage:
• Size: 902 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Machine Learning Projects with JavaScript

6 projects to understand basic algorithms in Machine Learning, using Lodash and Tensorflow.js.

| #   | Project                                      | Description                                                                                  |

| --- | -------------------------------------------- | -------------------------------------------------------------------------------------------- |

| 01  | [**Plinko**](#plinko)                        | A quick introduction to K-Nearest Neighbors (KNN) algorithm using Lodash.                    |

| 02  | [**House Pricings**](#housepricings)         | Another implementation of K-Nearest Neighbors (KNN) algorithm with Tensorflow.js.            |

| 03  | [**Gas Mileage Calculator**](#gasmileage)    | A gradient descent algorithm in linear regression using Tensorflow.js.                       |

| 04  | [**Smog Test**](#smogtest)                   | A gradient descent algorithm in logistic regression to handle natural binary classification. |

| 05  | [**Fuel Efficiency Class**](#fuelefficiency) | A multinominal logistic regression to handle multi-value classification.                     |

| 06  | [**Handwriting Recognition**](#handwriting)  | Another multinominal logistic regression with a larger dataset.                              |

##  1) Plinko

A quick introduction to K-Nearest Neighbors (KNN) algorithm using Lodash.

[See 01-plinko folder](01-plinko)



    

        

    



### Features

- implementing K-Nearest Neighbors (KNN) algorithm with Lodash methods.

- testing the algorithm and interpreting bad results.

- creating random test and training datasets.

- generalizing KNN, gauging accuracy and printing a report.

- investigating optimal k values.

- updating KNN for multiple features.

- applying normalization with MinMax.

- handling objective feature selection with KNN.

##  2) House Pricings

Another implementation of K-Nearest Neighbors (KNN) algorithm with Tensorflow.js.

[See 02-house-pricings folder](02-house-pricings)

### Features

- loading CSV data.

- running K-Nearest Neighbors (KNN) algorithm with Tensorflow.js.

- analyzing results and reporting error percentages.

- applying standardization.

- debugging calculations with node --inspect-brk and Chrome debugger.

- adding features to get a better accuracy.

##  3) Gas Mileage Calculator

A gradient descent algorithm in linear regression using Tensorflow.js.

[See 03-gas-mileage-calculator folder](03-gas-mileage-calculator)



    

        

    



### Features

- loading CSV data and creating a LinearRegression class.

- creating a basic gradient descent implementation with arrays of data and for loops.

- calculating mean squared error (MSE) slopes, updating coefficients and interpreting results.

- understanding matrix multiplication.

- vectorizing the gradient descent algorithm with Tensorflow.js helpers.

- gauging model accuracy by implementing the coefficient of determination.

- applying standardization and massaging learning rates.

- refactoring for multivariate regression.

- tracking MSE history to update learning rate automatically.

- plotting MSE history with nodeplotlib.

- refactoring towards batch and stochastic gradient descent for better performances.

- making predictions with the model.

##  4) Smog Test

A gradient descent algorithm in logistic regression to handle natural binary classification.

[See 04-smog-test folder](04-smog-test)



    

        

    



### Features

- updating linear regression for logistic regression by adding the sigmoid equation.

- implementing a test function to gauge classification accuracy.

- supporting variable decision boundaries.

- refactoring with cross entropy to update learning rate.

- plotting cost history with nodeplotlib.

##  5) Fuel Efficiency Class

A multinominal logistic regression to handle multi-value classification.

[See 05-fuel-efficiency folder](05-fuel-efficiency)

### Features

- refactoring to multi-column weights.

- classifying continuous values in 3 ranges of fuel consumption: low, medium and high.

- training a multinominal model.

- handling conditional probability distribution with the softmax equation.

- implementing accuracy gauges with argMax() and calculating accurary.

##  6) Handwriting Recognition

Another multinominal logistic regression with a larger dataset.

[See 06-handwriting-recognition folder](06-handwriting-recognition)



    

        

    



### Features

- flattening image data from MNIST handwritten digit database.

- encoding label values and implementing an accuracy gauge.

- debugging the calculation process with node --inspect-brk.

- dealing with zero variances.

- creating memory snapshots with Chrome debugger.

- releasing references to MNIST dataset.

- optimizing Tensorflow.js memory usage with tf.tidy().

- measuring footprint reduction.

- plotting cost history and improving model accuracy.

Based on [Machine Learning with JavaScript](https://www.udemy.com/course/machine-learning-with-javascript/) by Stephen Grider (2021).