The inclusion of script tags in HTML code when using TensorFlow.js in a web application serves a crucial purpose in harnessing the power of deep learning within the browser. TensorFlow.js, an open-source library developed by Google, enables developers to deploy machine learning models directly in the browser using JavaScript. By incorporating script tags, developers can seamlessly integrate TensorFlow.js into their web applications and leverage the capabilities of deep learning to perform various tasks, such as image recognition, natural language processing, and more.
When using TensorFlow.js, the script tags are utilized to import the required JavaScript files and libraries into the HTML document. These script tags serve as references to external JavaScript files that contain the necessary code for loading and executing TensorFlow.js functionalities. By including these script tags, developers ensure that the browser loads the required TensorFlow.js files and dependencies, enabling the execution of deep learning operations.
For instance, let's consider a basic TensorFlow.js web application that performs image classification. To implement this functionality, the developer would need to include the TensorFlow.js script tag in the HTML code. This script tag would reference the TensorFlow.js library, which contains the necessary functions and algorithms for image classification. By including this script tag, the web application gains access to the power of TensorFlow.js and can utilize its deep learning capabilities to classify images.
Additionally, script tags can also be used to import pre-trained models into the web application. TensorFlow.js allows developers to convert trained models from TensorFlow (Python) into a format that can be used directly in the browser. These pre-trained models can be loaded into the web application using script tags, enabling the application to perform complex tasks without the need for extensive training within the browser itself.
The purpose of including script tags in HTML code when using TensorFlow.js in a web application is to import the necessary JavaScript files, libraries, and pre-trained models. These script tags enable the web application to leverage the power of TensorFlow.js and perform deep learning tasks directly within the browser, such as image recognition, natural language processing, and more.
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