Here's how DOCS.JAX.DEV makes money* and how much!

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DOCS . JAX . DEV {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Docs.jax.dev Make Money
  6. Keywords
  7. Topics
  8. External Links
  9. Libraries
  10. Hosting Providers

We are analyzing https://docs.jax.dev/en/latest/notebooks/convolutions.html.

Title:
Generalized convolutions in JAX — JAX documentation
Description:
No description found...
Website Age:
4 years and 8 months (reg. 2020-11-01).

Matching Content Categories {📚}

  • Graphic Design
  • Photography
  • DIY & Home Improvement

Content Management System {📝}

What CMS is docs.jax.dev built with?

Custom-built

No common CMS systems were detected on Docs.jax.dev, but we identified it was custom coded using Bootstrap (CSS).

Traffic Estimate {📈}

What is the average monthly size of docs.jax.dev audience?

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


Based on our best estimate, this website will receive around 50,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

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How Does Docs.jax.dev Make Money? {💸}

We're unsure if the website is profiting.

Many websites are intended to earn money, but some serve to share ideas or build connections. Websites exist for all kinds of purposes. This might be one of them. Docs.jax.dev could be secretly minting cash, but we can't detect the process.

Keywords {🔍}

kernel, dilation, shape, output, tensor, image, channel, lhs, rhs, padding, jax, convolutions, window, conv, dimension, convolution, layout, mode, strides, printout, outshape, pltfigurefigsize, lhsimage, rhskernel, data, import, printfirst, pltimshownparrayout, dimensionnumbers, simple, permutation, basic, general, nhwc, laxconvgeneraldilatedimg, ndimensional, dtypejnpfloat, kernelshape, stride, valid, guides, aka, generalized, jaxnumpyconvolve, onedimensional, modesame, original, jnpnewaxis, laxconvwithgeneralpadding, shapes,

Topics {✒️}

aka multi-process/multi-host jax basic n-dimensional convolution n-dimensional convolution n-dimensional convolutions distributed data loading basic convolution operations host offloading introduction implement transposed convolutions multi-controller jax edge conv kernel hwio kernel convention simple 1d demo simple convenience functions '3d conv output' 1d smoothing implemented axis layout arguments custom output padding highly recommended reading simple synthetic image dimensional convolution kernel layout shard_map jax memories dimension permutation print jax import lax xla offer mode parameter controls dimension numbers jnp import numpy general convolutions important bit print input layout 1d convolutions 2d convolutions import matplotlib output layout dimensional case batched convolutions projection='3d' gaussian filter oihw kernels kernel += jnp generalized convolutions general types aka jax channel dimension image = jnp batch dimension kernel_rot = jnp image based sample image

Libraries {📚}

  • Bootstrap
  • Clipboard.js
  • Typed.js

Emails and Hosting {✉️}

Mail Servers:

  • aspmx.l.google.com
  • alt1.aspmx.l.google.com
  • alt2.aspmx.l.google.com
  • aspmx2.googlemail.com
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Name Servers:

  • ivan.ns.cloudflare.com
  • tegan.ns.cloudflare.com
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