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fix broken link in "NNs with TFDS" notebook

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Roy Frostig 2022-07-28 19:03:19 -07:00
parent 9f96a0474e
commit cd77debfa7
2 changed files with 2 additions and 2 deletions
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2
docs/notebooks/neural_network_with_tfds_data.ipynb
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@ -44,7 +44,7 @@
"\n",
"![JAX](https://raw.githubusercontent.com/google/jax/main/images/jax_logo_250px.png)\n",
"\n",
"Let's combine everything we showed in the [quickstart notebook](https://colab.research.google.com/github/google/jax/blob/main/notebooks/quickstart.ipynb) to train a simple neural network. We will first specify and train a simple MLP on MNIST using JAX for the computation. We will use `tensorflow/datasets` data loading API to load images and labels (because it's pretty great, and the world doesn't need yet another data loading library :P).\n",
"Let's combine everything we showed in the [quickstart notebook](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html) to train a simple neural network. We will first specify and train a simple MLP on MNIST using JAX for the computation. We will use `tensorflow/datasets` data loading API to load images and labels (because it's pretty great, and the world doesn't need yet another data loading library :P).\n",
"\n",
"Of course, you can use JAX with any API that is compatible with NumPy to make specifying the model a bit more plug-and-play. Here, just for explanatory purposes, we won't use any neural network libraries or special APIs for building our model."
]

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docs/notebooks/neural_network_with_tfds_data.md
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@ -42,7 +42,7 @@ _Forked from_ `neural_network_and_data_loading.ipynb`
![JAX](https://raw.githubusercontent.com/google/jax/main/images/jax_logo_250px.png)
Let's combine everything we showed in the [quickstart notebook](https://colab.research.google.com/github/google/jax/blob/main/notebooks/quickstart.ipynb) to train a simple neural network. We will first specify and train a simple MLP on MNIST using JAX for the computation. We will use `tensorflow/datasets` data loading API to load images and labels (because it's pretty great, and the world doesn't need yet another data loading library :P).
Let's combine everything we showed in the [quickstart notebook](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html) to train a simple neural network. We will first specify and train a simple MLP on MNIST using JAX for the computation. We will use `tensorflow/datasets` data loading API to load images and labels (because it's pretty great, and the world doesn't need yet another data loading library :P).
Of course, you can use JAX with any API that is compatible with NumPy to make specifying the model a bit more plug-and-play. Here, just for explanatory purposes, we won't use any neural network libraries or special APIs for building our model.