Here's how JAX.READTHEDOCS.IO makes money* and how much!

*Please read our disclaimer before using our estimates.
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JAX . READTHEDOCS . IO {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Jax.readthedocs.io Make Money
  6. Keywords
  7. Topics
  8. External Links
  9. Libraries
  10. CDN Services

We began analyzing https://docs.jax.dev/en/latest/, but it redirected us to https://docs.jax.dev/en/latest/. The analysis below is for the second page.

Title[redir]:
JAX: High performance array computing — JAX documentation
Description:
No description found...

Matching Content Categories {📚}

  • Technology & Computing
  • Science
  • Virtual Reality

Content Management System {📝}

What CMS is jax.readthedocs.io built with?

Custom-built

No common CMS systems were detected on Jax.readthedocs.io, but we identified it was custom coded using Bootstrap (CSS).

Traffic Estimate {📈}

What is the average monthly size of jax.readthedocs.io audience?

🚀 Good Traffic: 50k - 100k visitors per month


Based on our best estimate, this website will receive around 56,199 visitors per month in the current month.

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How Does Jax.readthedocs.io Make Money? {💸}

We see no obvious way the site makes money.

Not all websites focus on profit; some are designed to educate, connect people, or share useful tools. People create websites for numerous reasons. And this could be one such example. Jax.readthedocs.io could have a money-making trick up its sleeve, but it's undetectable for now.

Keywords {🔍}

jax, array, computing, transformations, program, numerical, machine, learning, familiar, api, installation, started, user, guides, neural, networks, stack, flax, ecosystem, tools, tensorflow, datasets, probabilistic, authors, skip, main, content, high, performance, python, library, acceleratororiented, computation, transformation, designed, highperformance, largescale, numpystyle, ease, adoption, researchers, engineers, includes, composable, function, compilation, batching, automatic, differentiation, parallelization,

Topics {✒️}

train neural networks numerical computing tools flax framework high-performance numerical computing accelerator-oriented array computation familiar numpy-style api large-scale machine learning jax-based libraries jax authors jax models ecosystem jax jax machine learning program transformation python library automatic differentiation code executes multiple backends including cpu narrowly-scoped evolving ecosystem small sample date list run skip designed ease adoption researchers engineers compilation batching parallelization gpu started start check implemented focuses built developed

External Links {🔗}(298)

Libraries {📚}

  • Bootstrap
  • Clipboard.js
  • Typed.js

CDN Services {📦}

  • Jsdelivr

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