Introduction to Tensorflow.js



Tensorflow.js is a framework which allows a user to implement machine learning models directly in web browser. Since it is capable to be rendered directly onto the browser.It has loads of advantages; Some of them are -

  • Browsers make the model more interactive
  • Browsers have sensors, meaning they have access to the computer's speaker and microphone so tinkering with those via web becomes easy
  • Wide distribution (every internet user has a browser)

Apart from these, Tensorflow.js has a very precise documentation and is pretty user friendly in nature. The best part is, since the library can be used directly with javascript, there's now no hassle to write a python model, wrap it in a flask server and deploy - which was the usual protocol earlier. Originated as deeplearn.js, it joined the tensorflow family on March, 2018 which was announced at Tensorflow dev summit.It has support of over 32+ layers and has can help create neural networks with minimal syntax by the virtue of the layers API. It has become an amazing tool for implementing basic neural networks in web, making the process web developers absolutely hassle free.

Tensorflow.js also makes it easier to serve models onto web for python users. It comes with a converter which can port a saved model written in python to a tensorflowjs model. This can now be used to serve model onto the web with very few lines of code.


Basic Idea about Neural Networks

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I am an undergraduate student at NSIT, New Delhi. I am a technocrat , who loves to code and wishes to learn new advancements in software and tech in general .Being involved with the field of Machine Learning and Artificial Intelligence for more than a year, I'm extremely passionate about it and wish to further my knowledge in the same field. It feels amazing to learn and implement web based technologies as it is easy to serve and integrate Machine Learning models in them. Apart from them I am also an avid opensource enthusiast and have been contributing to various communities like Debian and Python Software Foundation.

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Id: 1302
Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Intermediate
Last Updated: