Underscore vs ConvNetJS | What are the differences? | StackShare

ConvNetJS vs Underscore

Overview

Underscore

Stacks1.9K

Followers589

Votes290

GitHub Stars27.4K

Forks5.5K

ConvNetJS

Stacks19

Followers22

Votes0

GitHub Stars11.1K

Forks2.1K

Underscore vs ConvNetJS: What are the differences?

What is Underscore? JavaScript's utility _ belt. A JavaScript library that provides a whole mess of useful functional programming helpers without extending any built-in objects.

What is ConvNetJS? Deep Learning in your browser. It is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat.

Underscore and ConvNetJS belong to "Javascript Utilities & Libraries" category of the tech stack.

Underscore and ConvNetJS are both open source tools. Underscore with 24.9K GitHub stars and 5.43K forks on GitHub appears to be more popular than ConvNetJS with 9.8K GitHub stars and 1.95K GitHub forks.

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Advice on Underscore, ConvNetJS

Abigail

Dec 6, 2019

Decided

Fast Healthcare Interoperability Resources (FHIR) provides standard data objects in JSON format for the healthcare industry. Since JSON objects are hierarchical and tree-like, we had a need to defensively 'pluck' fields from our JSON objects and do lots of mapping. We tried jQuery and Underscore and a few other technologies like FHIRPath; but Lodash has been the most well supported, works in the most contexts, has the cleanest syntax, etc. We particularly like the ES6 version of Lodash, where we can import the method names directly, without resorting to * or _ syntax. We got hooked on the 'get' function to defensively pluck fields from objects without crashing our user interface, and have found countless uses for the other lodash functions throughout our apps. Lodash is great for developing and optimizing algorithms.

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Detailed Comparison

Underscore	ConvNetJS
A JavaScript library that provides a whole mess of useful functional programming helpers without extending any built-in objects.	It is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat.
-	Formulate and solve Neural Networks in Javascript;Training Deep Learning models (Neural Networks) entirely in your browser; No compilers, No installations;
Statistics
GitHub Stars 27.4K	GitHub Stars 11.1K
GitHub Forks 5.5K	GitHub Forks 2.1K
Stacks 1.9K	Stacks 19
Followers 589	Followers 22
Votes 290	Votes 0
Pros & Cons
Pros 85 Utility 55 Simple 40 Functional programming 32 Fast 28 Open source	No community feedback yet
Integrations
No integrations available	Node.js JavaScript Google Chrome

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