DOCS . JAX . DEV {}

Matching Content Categories {📚}

• Business & Finance
• Education
• Automotive

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 {📈}

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🚀 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.
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How Does Docs.jax.dev Make Money? {💸}

We find it hard to spot revenue streams.

Not every website is profit-driven; some are created to spread information or serve as an online presence. Websites can be made for many reasons. This could be one of them. Docs.jax.dev has a secret sauce for making money, but we can't detect it yet.

Keywords {🔍}

function, jax, automatic, jaxgrad, differentiation, gradients, advanced, differences, check, python, dtypefloat, eps, computing, respect, evaluating, derivatives, lossw, preds, targets, key, gradient, autodiff, tutorial, linear, logistic, regression, differentiating, lists, tuples, dicts, jaxvalueandgrad, differentiate, import, functions, dfdx, printdfdx, def, return, inputs, loss, wgrad, gradloss, previous, finite, unitvec, introduction, guides, section, taking, nested,

Topics {✒️}

compute higher-order derivatives jax-transformable python functions linear logistic regression single-variable case functional differential geometry standard python containers higher-order derivatives custom derivative rules automatic differentiation scalar-valued function negative log-likelihood 2nd order derivatives introductory autodiff topics simple convenience function random direction key advanced topics python expression jax primitives jax internals api finite differences numerical differences compute derivatives pytree abstraction advanced pytrees introduction f'{b_grad=}' compute gradients key = jax autodiff makes jit python function b_grad=array f'{w_grad=}' jax internals jax authors b_grad = grad logical operators fundamental applications critical part taking gradients hood” isn accessible video deeper sense stacking transformations def sigmoid def predict toy dataset training examples preds = predict positional arguments

Social Networks {👍}(1)

• https://www.youtube.com/watch?v=wG_nF1awSSY

External Links {🔗}(3)

• Explore the financials of https://github.com/jax-ml/jax
• How much money does https://mitpress.mit.edu/9780262028967/structure-and-interpretation-of-classical-mechanics generate?
• Earnings of https://mitpress.mit.edu/9780262019347/functional-differential-geometry

Libraries {📚}

• Bootstrap
• Clipboard.js
• Typed.js

Emails and Hosting {✉️}

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Name Servers:

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CDN Services {📦}

• Jsdelivr