JavaScript library Archives - Artificial Intelligence https://www.aiuniverse.xyz/tag/javascript-library/ Exploring the universe of Intelligence Thu, 27 Jul 2023 12:57:17 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 What is jQuery and How jQuery Works & Architecture? https://www.aiuniverse.xyz/jquery-works-and-architecture/ https://www.aiuniverse.xyz/jquery-works-and-architecture/#respond Thu, 27 Jul 2023 12:38:50 +0000 https://www.aiuniverse.xyz/?p=17485 What is jQuery ? jQuery is a lightweight, fast, small, and feature-rich JavaScript library that helps web developers work with HTML and make their websites more interactive Read More

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What is jQuery ?

jQuery is a lightweight, fast, small, and feature-rich JavaScript library that helps web developers work with HTML and make their websites more interactive and dynamic. It simplifies tasks like changing content, handling user actions, creating animations, and fetching data from the server without reloading the page. With jQuery, developers can achieve more with less code and ensure their websites work well across different browsers.

What is top use cases of jQuery ?

Some top use cases of jQuery include:

1. DOM manipulation:  jQuery provides a number of methods for manipulating the DOM, such as selecting elements, adding and removing elements, and changing the content of elements.

2. Event handling: jQuery provides a number of methods for handling events, such as mouse clicks, keyboard presses, and form submissions.

3. Animation: jQuery provides a number of methods for animating elements, such as fading in and out, sliding, and rotating.

4. AJAX requests:  jQuery provides a number of methods for making AJAX requests, which allow you to exchange data with a server without reloading the page.

5. UI components: jQuery offers a wide range of UI components like date pickers, sliders, and tooltips.

What are feature of jQuery ?

Some of the key features of jQuery include:

  • Shorthand syntax: jQuery’s syntax is much shorter and more concise than standard JavaScript, making it easier to write and read.
  • Efficiency: jQuery is very efficient, making it a good choice for performance-sensitive applications.
  • Cross-platform compatibility: jQuery is cross-platform, meaning that it can be used on any platform that supports JavaScript.
  • Extensive library: jQuery has a large and extensive library of methods, making it a powerful tool for web development.

What is the workflow of jQuery ?

The workflow of jQuery typically involves:

1. Include the jQuery library in your HTML file using a `<script>` tag.

2. Select HTML elements using jQuery selectors.

3. Perform actions or manipulate the selected elements using jQuery methods and functions.

4. Handle events triggered by the user or browser.

5. Make AJAX requests to retrieve or send data to servers.

How jQuery Works & Architecture?

How to Install and Configure jQuery ?

To install and configure jQuery, follow these steps:

  1. Download the jQuery library:
  • Visit the official jQuery website at https://jquery.com/.
  • Click on the “Download” button to get the latest version of jQuery.
  • Choose either the compressed or uncompressed version based on your preference.
  1. Include jQuery in your HTML file:
  • Copy the downloaded jQuery file into your project directory.
  • Add the following line within the <head> section of your HTML file:
    <script src="path/to/jquery.js"></script>
  • Replace “path/to/jquery.js” with the actual path to the jQuery file.
  1. Verify the installation:
  • Open your HTML file in a web browser.
  • Right-click on the page and select “Inspect” or press Ctrl + Shift + I to open the browser’s Developer Tools.
  • In the Developer Tools, navigate to the “Console” tab.
  • Type jQuery or $ into the console and press Enter. If jQuery is correctly installed, it should return the jQuery version number.

You have now installed jQuery and can start using its libraries and functions in your web project. Remember to properly link to the jQuery file in any HTML file where you need to use jQuery functionality.

Step by Step Tutorials for jQuery for hello world program

For a step-by-step tutorial and a “Hello, World!” program using jQuery, here’s a basic example:

Step 1: Create an HTML file, for example, “index.html”.

Step 2: Include the jQuery library by adding the following code inside the `<head>` tag:


<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>

Step 3: Add a `<script>` section below the jQuery inclusion to write your JavaScript code. For example:


<script>
$(document).ready(function() {
  // This function will be executed once the DOM is fully loaded
  $("button").click(function() {
    // This function will be executed when a button is clicked
    alert("Hello, World!");
  });
});
</script>

Step 4: Add a `<button>` element in the body of your HTML file:


<body>
  <button>Click me</button>
</body>

Step 5: Save and open the HTML file in a web browser. Whenever the button is clicked, it will trigger an alert saying “Hello, World!”.

In Another Example

Here is a step-by-step tutorial for creating a “Good Morning” program using jQuery:

  1. Create a new HTML file.
  2. Add the following code to the HTML file:

HTML

<html>
 <head>
  <title>Good Morning</title>
  <script src="https://code.jquery.com/jquery-3.5.1.min.js"></script>
 </head>
 <body>
  <h1>Good Morning</h1>
 </body>
</html>
  1. Save the HTML file.
  2. Open the HTML file in a web browser.

You should see the text “Good Morning” displayed on the web page.

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]]> https://www.aiuniverse.xyz/jquery-works-and-architecture/feed/ 0 https://www.aiuniverse.xyz/448-2/ https://www.aiuniverse.xyz/448-2/#comments Thu, 03 Aug 2017 07:36:54 +0000 http://www.aiuniverse.xyz/?p=448 Source – infoworld.com There’s now a JavaScript library for executing neural networks inside a webpage, using the hardware-accelerated graphics API available in modern web browsers. Developed by a team Read More

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There’s now a JavaScript library for executing neural networks inside a webpage, using the hardware-accelerated graphics API available in modern web browsers.

Developed by a team of MIT graduate students, TensorFire can run TensorFlow-style machine learning models on any GPU, without requiring the GPU-specific middleware typically needed by machine learning libraries such as Keras-js.

TensorFire is another step towards making machine learning available to the broadest possible audience, using hardware and software people are already likely to possess, and via advances in how accurate model predictions can be served with a fraction of the resources previously needed.

The machine learning power is in your browser

TensorFire works using the WebGL standard, a cross-platform system for rendering GPU-accelerated graphics in browsers. WebGL supports GLSL, a C-like language used to write shaders, which are short programs used to transform data directly on the GPU.

Shaders are typically used in the WebGL pipeline to transform how graphics are rendered—for example, to render shadows or other visual effects. But TensorFire uses shaders to run in parallel the computations needed to generate predictions from TensorFlow models. TensorFire also comes with a library for importing existing TensorFlow and Keras models.

With this framework, you can deploy a trained model directly into a web browser and serve predictions locally from the browser. The user doesn’t need to download, install, or compile anything; all the work is done directly in the browser. The data used to make the predictions is also processed entirely on the client. The brand of GPU doesn’t matter, either: Both AMD and Nvidia GPUs are supported.

One web-based example of TensorFire shows a style-transfer neural network, where the style of one piece of artwork can be mapped to another image. The slowest part of the demo is downloading the model and compiling the shader pipeline; the actual execution takes only a second or two.

TensorFire’s creators claim it’s faster than other solutions. Bouncing data between GPU and CPU is a common performance bottleneck, and so TensorFire avoids this by keeping as much data as possible on the GPU at a time.

Keep your data close and your predictions closer

The most prominent advantages of TensorFire’s approach are its portability and convenience. Modern web browsers run on most every operating system and hardware platform, and even low-end smartphones have generous amounts of GPU power to spare. Much of the work involved in getting useful results from machine learning models is setting up the machine learning pipeline, either to perform the training or to deliver the predictions. It is very useful to boil much of that process down to just opening up a web browser and clicking something, at least for certain classes of jobs.

Another advantage claimed by TensorFire’s creators is that it allows the deployment of predictions to be done entirely on the client. This won’t be as much of an advantage where both the trained model and the data are already deployed to the cloud. But it’s a good fit for applications where the deployed model is small, the data is client-side, and the user is uneasy about uploading anything.

A third advantage of TensorFire is that it theoretically loosens the restrictions on which brands of graphics cards can be used for machine learning, thanks to the high speed it gains from both Nvidia and AMD GPUs.

Historically, Nvidia’s CUDA standard has been the go-to for accelerating machine learning via GPUs, providing more performance than the more open-ended OpenCL standard, which supports a broad range of hardware. AMD has its own plans about how to work around OpenCL’s performance issues, but TensorFire lets users and developers sidestep the issue completely.

TensorFire also takes advantage of another growing phenomenon: making machine learning models more compact and efficient with a slight (typically undetectable) loss of accuracy. This “low-precision quantized tensor” approach means smaller models can be deployed to the client, and predictions can be made faster.

But TensorFire’s makers claim the “low-precision quantized tensor” approach allows the software to run on a broader range of GPUs and browsers, especially those that don’t support the full range of WebGL extensions.

Finally, the TensorFire team plans to release the library as an MIT-licensed open source project, so the acceleration work done in TensorFire could also be used in a broad range of applications—even those that don’t have anything to do with TensorFlow or machine learning. The framework’s creators note that the low-level GLSL API in TensorFire “can also be used to do arbitrary parallel general-purpose computation,” meaning that other frameworks for GPU-powered, in-browser, client-side computation could be built atop it.

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