TensorFlow has recently introduced a phenomenal library called the tensoflow.js. It uses Tensors as the core data concepts for its implementation. TensorFlow.js can also be used to run existing models available to us, which is fantastic, as a developer may want to use a keras/tensorflow model that may have been trained offline. With this launch by google, the TensorFlow.js has become to most popular library available today.
For the best usage, the recently released NodeJS bindings for TensorFlow has been very benefitial. As it allows us to work on both NodeJS and Browser. A very useful advantage of using this is that the mobile sensors can even provide sensor data to the model. Another advantage is that it supports GPU acceleration. We can also retain the existing machine learning models using data connected to the browser or the other side of the client.
Using TensorFlow.js in your project:
The best and the easiest way of using the TensorFlow.js is to add the code in the bold inside the script tags as shown:
<script src=”https://cdn.jsdelivr.net/npm/@firstname.lastname@example.org″> </script>
The main code for cleaning, testing, predicting and running the machine learning model is written between the script tags. For example:
. #tensorflow code body
*Also follow up this link to check tutorials and docs for tensorflow.js: https://js.tensorflow.org/
*Also check out GitHub for examples.