https://takao.blog/en/tags/tensorflow/Recent content in Tensorflow on Commentary of TakaoHugo -- gohugo.ioenCommentary of TakaoSat, 13 Jun 2026 23:11:50 +0900https://takao.blog/en/web/ml-in-browser/Tue, 30 Jul 2024 00:00:00 +0900https://takao.blog/en/web/ml-in-browser/<img src="https://takao.blog/img/thumnail.webp" alt="Featured image of post Machine Learning in the Browser with TensorFlow.js" /><p>Machine learning in the browser eliminates server costs, preserves user privacy, and enables offline-capable intelligent applications. TensorFlow.js brings ML to JavaScript developers with GPU-accelerated inference and training, powered by WebGL and WebGPU backends. This article covers loading pre-trained models, transfer learning, real-time pose detection, and production deployment considerations.</p> <h2 id="why-ml-in-the-browser">Why ML in the Browser? </h2><p>Running ML models client-side offers four key advantages: zero server costs (inference runs on the user&rsquo;s device), complete privacy (data never leaves the machine), offline capability (no network required after model load), and low latency (no round-trip for predictions). The trade-offs include limited compute power, memory constraints, battery drain on mobile devices, and large model download sizes (5-200 MB).</p>