The future of machine learning using JavaScript
summary
Argues that full ML pipelines can run in-browser using TensorFlow.js, Brain.js or ml5 with GPU.js fallback, and highlights privacy by keeping inference client-side. Demonstrates concrete results: trains on the Iris dataset (150 rows, 4 features) for 100 epochs in the page, and builds a pose-detection demo that reads keypoint (x,y) coordinates, computes joint angles to evaluate squat form and successfully recognizes repetitions. Notes runtimes via WebGL/WebAssembly and emerging WebGPU (preview shows large speedups) and deployment on Node.js and React Native.
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chapters (6)
- 0:00Introduction to ML in JavaScript
- 1:50Why use JavaScript for ML
- 3:16Key ML libraries in JavaScript
- 4:42Building ML models in JS
- 8:18Exploring ML with computer vision
- 11:14Future of ML in JavaScript
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