Edit JSON Online

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https://jsoneditoronline.org/

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About

Version: 5.10.8 (changelog), copyright © 2011-2022 Jos de Jong

JSON Editor Online is a web-based tool to view, edit, and format JSON. It shows your data side by side in a clear, editable treeview or a code editor. You can store documents locally or in the cloud.

Report a bug

If you encounter a bug or have questions or suggestions for improvements, please report this at https://github.com/josdejong/jsoneditor/issues/.

Source code

The source code of the main component of this web application, JSONEditor, is open source. You can find it at https://github.com/josdejong/jsoneditor.

Data policy

No data is shared anywhere except when you save a document in the cloud. Cloud documents are publicly accessible for anyone who has the document id.

DO NOT SAVE SENSITIVE DATA USING THE CLOUD FUNCTIONALITY

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This site uses cookies and similar tracking technologies such as the Common ID cookie to provide its services. Click here for information or to opt out.

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Machine Learning on Tabular Data with Nyckel

. In the video, the instructor used one of the most popular datasets on kaggle - the titanic dataset. It lists data on titanic passengers (name, age, number of siblings, ticket class, etc.) with a column that indicates whether the passenger survived the shipwreck. The challenge is to create a model that predicts which passengers survived. In less that two minutes, the system is able to go from the data to a trained and deployed model. Underneath this serene user experience, there is a lot going on: (1)Training-optimized cloud hardware is allocated, (2)Several state-of-the-art deep neural networks and hyper-parameters are evaluated to find the best fit for the data, (3)The model is deployed on inference-optimized and elastically scalable cloud hardware, behind a secure REST API . . https://www.nyckel.com/blog/fast-machine-learning-tabular-data-automl/

Getting Started with ML5.js — Tutorial Series

 

Part 1 - https://medium.com/aixdesign/getting-started-with-ml5-js-tutorial-part-i-image-classifier-6d437ec38045

Part 2- https://medium.com/aixdesign/getting-started-with-ml5-js-tutorial-part-ii-posenet-ca142c2e05d1

Part 3 - https://medium.com/aixdesign/getting-started-with-ml5-js-tutorial-part-iii-yoga-pose-detection-8b4609f122ab

Part 4 - https://medium.com/aixdesign/getting-started-with-ml5-js-tutorial-part-iv-yoga-pose-detection-bb4a447ab197

How To: Build Simple Neural Network With TensorFlow JavaScript To Recognize Number Digit Image

 

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In this article, we will learn how to deploy a machine learning model using NodeJS. While doing so we will make a simple handwritten digit recognizer using NodeJS and tensorflow.js.

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Tensorflow.js is an ML library for JavaScript. It helps to deploy machine learning models directly into node.js or a web browser.

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Training the Model: For training the model we are going to use Google Colab. It is a platform where we can run all our python code, and it comes loaded with most of the machine learning libraries that are used.

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https://www.geeksforgeeks.org/how-to-deploy-a-machine-learning-model-using-node-js/

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TensorFlow.Js Tutorial: Making Predictions from 2D Data

This tutorial is based on the original article at: https://codelabs.developers.google.com/codelabs/tfjs-training-regression

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<!-- https://codelabs.developers.google.com/codelabs/tfjs-training-regression/ --> <html> <head> <title>TensorFlow.js Tutorial</title> <!-- Import TensorFlow.js --> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <!-- Import tfjs-vis --> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis@1.0.2/dist/tfjs-vis.umd.min.js"></script> </head> <body> <!-- Import the main script file --> <script src="script.js"></script> </body> </html>

javascript

/* 1) Define run() Function */ async function run() { /* 4) Call getData() function */ /* 5) Render Scatterplot Graph */ /* 7) Call createModel() for NN */ /* 9) Call convertToTensor() */ /* 11) Call trainModel() */ /* 13) Call testModel() */ } /* 2) Define eventListener() For run() Function */ document.addEventListener('DOMContentLoaded', run); /* 3) Define getData() function */ /** * Get the car data reduced to just the variables we are interested * and cleaned of missing data. */ async function getData() { } /* 6) Define createModel() function for NN */ function createModel() { } /* 8) Define convertToTensor() */ /** * Convert the input data to tensors that we can use for machine * learning. We will also do the important best practices of _shuffling_ * the data and _normalizing_ the data * MPG on the y-axis. */ function convertToTensor(data) { } /* 10) Define trainModel() */ async function trainModel(model, inputs, labels) { } /* 12) Define testModel() */ function testModel(model, inputData, normalizationData) { }

See the Pen Untitled by smartcomputing123 (@smartcomputing123) on CodePen.

How to: show code outline in Visual Studio?

 

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1) View -> Open View

2) select (or type) Outline

3) You'll now get an Outline palette that shows full information on the class including properties and functions.

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How to: run code directly from Git Hub

GitHub allows online users to share files but they cannot be directly loaded and executed. The solution to this ie for loading files such as JS and CSS is to load through RawGit. RawGit serves raw files directly from GitHub with proper Content-Type headers.

Steps:

First:- Sign up With GitHub

Second:- Creating a Repository on GitHub

Third:- Creating a GitHub Project to host JS and CSS files

Fourth:- Adding GitHub file or gist URL on RawGit for serving file on our Website.

REFERENCE: 

https://www.bloggerspice.com/2016/09/host-JavaScript-and-CSS-files-on-gitHub-through-rawgit.html