TypeScript for Machine Learning: An Introduction


Machine learning has become a crucial part of various applications, and TypeScript can be a valuable addition to your machine learning projects. In this guide, we'll introduce TypeScript for machine learning and provide a basic example of using TypeScript with TensorFlow.js for data processing.


Before you begin, make sure you have the following prerequisites:

  • Node.js: You can download it from https://nodejs.org/
  • Visual Studio Code (or your preferred code editor)

Getting Started with TypeScript for Machine Learning

Let's start with a basic example of using TypeScript for data processing with TensorFlow.js.

Step 1: Set Up Your Project

Create a new directory for your project and navigate to it in your terminal:

mkdir ml-with-typescript
cd ml-with-typescript

Step 2: Initialize a Node.js Project

Initialize a Node.js project and answer the prompts. You can use the default settings for most prompts:

npm init

Step 3: Install Dependencies

Install the required dependencies, including TypeScript and TensorFlow.js:

npm install typescript tensorflow node-fetch @tensorflow/tfjs-node @tensorflow/tfjs-node-gpu --save

Step 4: Create TypeScript Configuration

Create a TypeScript configuration file (tsconfig.json) in your project directory:

"compilerOptions": {
"target": "ES6",
"outDir": "./dist",
"rootDir": "./src"

Step 5: Create TypeScript Code

Create a TypeScript file (e.g., app.ts) to demonstrate data processing with TensorFlow.js:

// app.ts
import * as tf from '@tensorflow/tfjs-node';
import fetch from 'node-fetch';
async function run() {
const response = await fetch('https://raw.githubusercontent.com/tensorflow/tfjs-examples/master/mnist-data/mnist.json');
const data = await response.json();
const trainData = tf.tensor(data.trainData);
const testData = tf.tensor(data.testData);

Step 6: Build and Run the Project

Compile your TypeScript code into JavaScript using the TypeScript Compiler (tsc):

npx tsc

Run your TypeScript code:

node ./dist/app.js

This example fetches data from TensorFlow.js's MNIST dataset, converts it to TensorFlow tensors, and prints the data. In a real-world machine learning project, you would perform more advanced tasks like model training and inference.


Using TypeScript for machine learning projects offers the benefits of strong typing and improved code maintainability. This introduction covered basic data processing with TensorFlow.js. In real-world machine learning applications, you can utilize TypeScript for data manipulation, model development, and integration with various libraries and frameworks.