DOCS . JAX . DEV {}

Matching Content Categories {📚}

• Education
• Careers
• Virtual Reality

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 50,213 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.

The purpose of some websites isn't monetary gain; they're meant to inform, educate, or foster collaboration. Everyone has unique reasons for building websites. This could be an example. Docs.jax.dev has a revenue plan, but it's either invisible or we haven't found it.

Keywords {🔍}

set, accuracy, training, jax, data, test, epoch, neural, network, sec, loading, license, def, return, function, tensorflowdatasets, import, simple, images, numpy, lets, train, guides, gpu, api, version, jit, vmap, params, image, activations, loss, numlabels, numpixels, computation, autobatching, authors, great, grad, sizes, stepsize, batchsize, predictions, single, predictparams, preds, randomflattenedimages, predict, accuracyparams, targets,

Topics {✒️}

tensorflow/datasets training loop tensorflow/datasets data loader auto-batching predictions gpu memory fully-connected neural network automatically handle mini-batches compilation exporting performance penalty include data loading tensorflow/datasets jax data loading library neural network libraries neural network parameters great data loaders simple neural network jax import grad auto-batched version jax api randomly initialize weights accelerator-backed numpy tensorflow/datasets test_data = mnist_data['train'] org/licenses/license-2 import tensorflow test_labels = test_data['image'] def init_network_params def accuracy import jax device_type='gpu' load returns tf computation neural network return [random_layer_params train_labels = train_data['image'] auto-vectorization handle batches data_dir=data_dir jax library def loss 2f} sec jax authors def predict mnist_data = tfds info = tfds special apis import tensorflow_datasets jax package loss function activations = relu helper function

External Links {🔗}(3)

• Check the income stats for https://github.com/jax-ml/jax
• How much does https://colab.research.google.com/github/jax-ml/jax/blob/main/docs/notebooks/neural_network_with_tfds_data.ipynb pull in monthly?
• How much does https://kaggle.com/kernels/welcome?src=https://github.com/jax-ml/jax/blob/main/docs/notebooks/neural_network_with_tfds_data.ipynb bring in each month?

Libraries {📚}

• Bootstrap
• Clipboard.js
• Typed.js

Emails and Hosting {✉️}

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Name Servers:

• ivan.ns.cloudflare.com
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