In practice, I believe that in most cases rather than creating models from scratch you will create models which already trained and solve a problem that is close to yours. This technique is called Transfer Learning. As you may already know, one big issue of training models from scratch is that we need to collect and label a huge amount of data and it's pretty time-consuming work that may be not affordable for your project. Also, it is computationally very expensive to train a neural network on millions of images and it may require weeks of training on multiple GPUs. The mental workflow of Transfer Learning is depicted in figure 1.

The graph represents a network of 147 Twitter users whose tweets in the requested range contained "tensorflowjs", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 02 May 2021 at 03:48 UTC. The requested start date was Sunday, 02 May 2021 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 7-hour, 18-minute period from Sunday, 18 April 2021 at 14:20 UTC to Saturday, 01 May 2021 at 21:39 UTC.

Demo of application is available and sources are on my github. React is one of the most used javascript libraries to build a rich and powerful user interface. Tensorflowjs is the implementation of Tensorflow for NodeJS and to allow developers to use client-side machine learning models created on the server side. Node.js must be installed to generate the project and manage dependencies. The application is developped with React library, we will use Create React App to create quickly the project, with a minimal configuration.