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TensorFlow.js is a JavaScript library for training and deploying Machine Learning (ML) models in the browser (client-side) and on Node.js (server-side). In this article, I want to describe my experience in building an App (that I recently published on Google Play Store) with this javascript ML library.

However, instead of just going straight into how I built this app using TensorFlow.js, I want to first describe the conditions/needs that led to this choice. I also want to touch a little bit on some other approaches that I realized could be possible that you can take to build an App leveraging machine learning. Finally, I want to leave behind some lessons I learned through this experience and would love your thoughts if you have been experimenting with ML in Apps.


Source de l’article sur DZONE (AI)

This GitHub project is a highly interactive graphic demonstration of the features and operations of a generated adversarial network using TensorFow.js. It is based on work done by Minsuk Kahng, Nikhil Thorat, Duen Horng (Polo) Chau, Fernanda B. Viegas, and Martin Wattenberg in their paper: GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation.

Full disclosure: The data sets are small enough and simple enough to demonstrate the technology but clearly are not full-blown image processing examples, which can often consume a lot (CPU/years?) of computer time. But these small examples provide an excellent introduction into what is going on. It takes away the spooky magic…which is a good thing! 


Source de l’article sur DZONE (AI)