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bundlejs VS Machine Learning Playground

Compare bundlejs VS Machine Learning Playground and see what are their differences

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bundlejs logo bundlejs

A quick and easy way to bundle, minify, and compress (gzip and brotli) your ts, js, jsx and npm projects all online, with the bundle file size.

Machine Learning Playground logo Machine Learning Playground

Breathtaking visuals for learning ML techniques.
  • bundlejs Landing page
    Landing page //
    2022-04-23

bundle is a quick and easy way to bundle your projects, minify and see it's gzip size. It's an online tool similar to bundlephobia, but bundle does all the bundling locally on you browser and can treeshake and bundle multiple packages (both commonjs and esm) together, all without having to install any npm packages and with typescript support.

If there is something I missed, a mistake, or a feature you would like added please create an issue or a pull request and I'll try to get to it. You can contribute to this project at okikio/bundle.

You can join the discussion on Github discussions or Twitter.

You can now use search queries in bundle, all you need to do is add this to the url
?q={packages}&treeshake={methods to treeshake}

e.g.
You want react, react-dom, vue, and @okikio/animate, but only want the Animate and toStr methods exported from @okikio/animate.

You would add this to the url bundlejs.com/?q=react,react-dom,vue,@okikio/animate&treeshake=[*],[*],[*],[{Animate,toStr}]

  • Machine Learning Playground Landing page
    Landing page //
    2019-02-04

bundlejs

Website
bundlejs.com
$ Details
free
Platforms
Web Google Chrome Firefox Safari JavaScript Edge
Release Date
2021 May
Edit details

Machine Learning Playground

Website
ml-playground.com
$ Details
-
Platforms
-
Release Date
-
Edit details

bundlejs features and specs

  • brotli
  • gzip
  • lz4
  • npm
  • deno
  • Configurable
  • jsx
  • TypeScript
  • Offline
  • Error and warning alerting
  • Open-source

Machine Learning Playground features and specs

  • User-Friendly Interface
    The platform offers an intuitive, easy-to-navigate interface that caters to both beginners and experienced machine learning practitioners.
  • Interactive Learning
    Users can experiment with various machine learning models in real-time, which facilitates hands-on learning and understanding of concepts.
  • No Installation Required
    Since it's a web-based platform, there is no need to install additional software, making it easily accessible from any device with an internet connection.
  • Pre-configured Environments
    The ML Playground provides pre-configured environments and datasets, saving time and effort in setting up the initial stages of a project.
  • Community Support
    A supportive community and plenty of resources are available to help users resolve issues or get guidance on their projects.

Possible disadvantages of Machine Learning Playground

  • Limited Customization
    The platform might not offer the depth of customization and flexibility required for more advanced or specialized machine learning projects.
  • Performance Constraints
    Being a web-based tool, it may face performance limitations when dealing with very large datasets or computationally intensive models.
  • Dependence on Internet Connection
    Since it is online, users are dependent on a stable internet connection, which could be a hindrance in areas with poor connectivity.
  • Data Privacy
    Uploading sensitive data to an online platform could pose privacy risks, which might be a concern for users handling confidential information.
  • Feature Limitations
    Certain advanced features and functionalities available in more comprehensive machine learning environments might be missing or limited on this platform.

Analysis of Machine Learning Playground

Overall verdict

  • Overall, Machine Learning Playground is considered a good resource for learning and experimenting with machine learning due to its comprehensive features, intuitive interface, and educational value.

Why this product is good

  • Machine Learning Playground (ml-playground.com) is often praised for its interactive and user-friendly environment, which makes it accessible for both beginners and experienced users to experiment with machine learning models. The platform provides numerous tutorials and resources that can help users understand complex concepts in a structured way. Additionally, it supports hands-on learning, which is crucial for grasping the practical aspects of machine learning.

Recommended for

  • Beginners interested in machine learning
  • Students looking for a practical learning tool
  • Educators who want to supplement their teaching materials
  • Data enthusiasts looking for a hands-on platform
  • Professionals seeking to refresh their knowledge of basic concepts

bundlejs videos

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Machine Learning Playground videos

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Machine Learning Playground Demo

Category Popularity

0-100% (relative to bundlejs and Machine Learning Playground)

bundlejs

Machine Learning Playground

Developer Tools
15 15%
Developer Tools
85% 85
AI
0 0%
AI
100% 100
Web Application Bundler
100 100%
Web Application Bundler
0% 0
JavaScript Tools
100 100%
JavaScript Tools
0% 0

User comments

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Social recommendations and mentions

Based on our record, bundlejs seems to be more popular. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

bundlejs mentions (8)

  • Zod 4
    These numbers don't reflect anything useful. This is the total size of the code in the package, most of which will be tree-shaken. In Zod's case, the package now contains three independent sub-libraries. I recommend plugging a script into bundlejs.com[0] to see bundle size numbers for a particular script [0] https://bundlejs.com. - Source: Hacker News / 24 days ago
  • PackagePhobia – Find the cost of adding a new dev dependency to your project
    [bundlejs](https://bundlejs.com/) is the better alternative to check your dependency sizes with. - Source: Hacker News / 3 months ago
  • ESM & CJS: The subtle shift in bundlejs' behaviour
    I was closing out some long lived issues over on bundlejs, when issue #50 reminded me of the ongoing debate about how bundlejs should handle the ESM and CJS packages. - Source: dev.to / almost 2 years ago
  • TANStack Query
    Still, I'm not really sure about its dependencies: it lists react and @tanstack/react-query (as opposed to @tanstack/query-core) and bundlejs reports 124KB gzipped. Also, while using it, you still need to refer to their react docs (that documentation is really good and has a lot of examples) but not everyone will be thrilled about checking a react documentation when they're using an angular package. Source: almost 2 years ago
  • Jest not recommended to be used in Node.js due to instanceOf operator issues
    It's somewhere in between. React as a lib and architecture _is_ platform-agnostic. The core logic is defined in the `react-reconciler` package. It contains all the implementation of rendering components, diffing trees, managing state, and running effects, as well as all the "Suspense" implementation. However, the way `react-reconciler` works is that it's built _into_ each platform-specific renderer... - Source: Hacker News / almost 2 years ago
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Machine Learning Playground mentions (0)

We have not tracked any mentions of Machine Learning Playground yet. Tracking of Machine Learning Playground recommendations started around Mar 2021.

What are some alternatives?

When comparing bundlejs and Machine Learning Playground, you can also consider the following products

BundlePhobia - Find the performance impact of adding a npm package to your bundle.

Amazon Machine Learning - Machine learning made easy for developers of any skill level

Webpack - Webpack is a module bundler. Its main purpose is to bundle JavaScript files for usage in a browser, yet it is also capable of transforming, bundling, or packaging just about any resource or asset.

Lobe - Visual tool for building custom deep learning models

esbuild - An extremely fast JavaScript bundler and minifier

Apple Machine Learning Journal - A blog written by Apple engineers

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