Rename getting_started_with_jax_for_AI files and URL to neural_net_basics

Author: skye

docs/source/JAX_Vision_transformer.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "This tutorial draws inspiration from the HuggingFace [Image classification tutorial](https://huggingface.co/docs/transformers/tasks/image_classification). The original JAX-based implementation of the ViT model can be found in the [google-research/vision_transformer](https://github.com/google-research/vision_transformer/) GitHub repository.\n",
     "\n",
-    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with Flax, Optax and JAX."
+    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with Flax, Optax and JAX."
    ]
   },
   {

docs/source/JAX_Vision_transformer.md

@@ -20,7 +20,7 @@ This tutorial guides you through developing and training a Vision Transformer (V
 
 This tutorial draws inspiration from the HuggingFace [Image classification tutorial](https://huggingface.co/docs/transformers/tasks/image_classification). The original JAX-based implementation of the ViT model can be found in the [google-research/vision_transformer](https://github.com/google-research/vision_transformer/) GitHub repository.
 
-If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with Flax, Optax and JAX.
+If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with Flax, Optax and JAX.
 
 +++
 

docs/source/JAX_for_LLM_pretraining.ipynb

@@ -20,7 +20,7 @@
     "- Train the model on Google Colab’s Cloud TPU v2\n",
     "- Profile for hyperparameter tuning\n",
     "\n",
-    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html)."
+    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html)."
    ]
   },
   {

docs/source/JAX_for_LLM_pretraining.md

@@ -27,7 +27,7 @@ Here, you will learn how to:
 - Train the model on Google Colab’s Cloud TPU v2
 - Profile for hyperparameter tuning
 
-If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html).
+If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html).
 
 +++ {"id": "hTmz5Cbco7n_"}
 

docs/source/JAX_transformer_text_classification.ipynb

@@ -18,7 +18,7 @@
     "- Train the model.\n",
     "- Evaluate the model with an example.\n",
     "\n",
-    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html).\n",
+    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html).\n",
     "\n",
     "## Setup\n",
     "\n",

docs/source/JAX_transformer_text_classification.md

@@ -26,7 +26,7 @@ Here, you will learn how to:
 - Train the model.
 - Evaluate the model with an example.
 
-If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html).
+If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with [Flax NNX](https://flax.readthedocs.io/en/latest/nnx_basics.html).
 
 ## Setup
 

docs/source/JAX_visualizing_models_metrics.ipynb

@@ -13,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To keep things straightforward and familiar, we reuse the model and data from [Getting started with JAX for AI](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html) - if you haven't read that yet and want the primer, start there before returning.\n",
+    "To keep things straightforward and familiar, we reuse the model and data from [Getting started with JAX for AI](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html) - if you haven't read that yet and want the primer, start there before returning.\n",
     "\n",
     "All of the modeling and training code is the same here. What we have added are the tensorboard connections and the discussion around them."
    ]

docs/source/JAX_visualizing_models_metrics.md

@@ -18,7 +18,7 @@ kernelspec:
 
 +++
 
-To keep things straightforward and familiar, we reuse the model and data from [Getting started with JAX for AI](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html) - if you haven't read that yet and want the primer, start there before returning.
+To keep things straightforward and familiar, we reuse the model and data from [Getting started with JAX for AI](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html) - if you haven't read that yet and want the primer, start there before returning.
 
 All of the modeling and training code is the same here. What we have added are the tensorboard connections and the discussion around them.
 

docs/source/conf.py

@@ -52,7 +52,7 @@
     'build/jupyter_execute',
     # Exclude markdown sources for notebooks:
     'digits_vae.md',
-    'getting_started_with_jax_for_AI.md',
+    'neural_net_basics.md',
     'JAX_for_PyTorch_users.md',
     'JAX_porting_PyTorch_model.md',
     'digits_diffusion_model.md',

docs/source/digits_diffusion_model.ipynb

@@ -20,7 +20,7 @@
     "- Train the model (with Google Colab’s Cloud TPU v2)\n",
     "- Visualize and track the model’s progress\n",
     "\n",
-    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with Flax, Optax and JAX."
+    "If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with Flax, Optax and JAX."
    ]
   },
   {

docs/source/digits_diffusion_model.md

@@ -27,7 +27,7 @@ In this tutorial, you'll learn how to:
 - Train the model (with Google Colab’s Cloud TPU v2)
 - Visualize and track the model’s progress
 
-If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which covers neural network building with Flax, Optax and JAX.
+If you are new to JAX for AI, check out the [introductory tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which covers neural network building with Flax, Optax and JAX.
 
 +++ {"id": "gwaaMmjXt7n7"}
 

docs/source/digits_vae.ipynb

@@ -10,9 +10,9 @@
     "\n",
     "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/digits_vae.ipynb)\n",
     "\n",
-    "This tutorial explores a simplified version of a generative model called [Variational Autoencoder (VAE)](https://en.wikipedia.org/wiki/Variational_autoencoder) with [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset, and expands on what we learned in [Getting started with JAX](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html). Along the way, you'll learn more about how JAX's [JIT compilation](https://jax.readthedocs.io/en/latest/jit-compilation.html#jit-compilation) (`jax.jit`) actually works, and what this means for [debugging](https://jax.readthedocs.io/en/latest/debugging/index.html) [JAX programs](https://jax.readthedocs.io/en/latest/debugging.html), as we learn how to identify what can go wrong during model training.\n",
+    "This tutorial explores a simplified version of a generative model called [Variational Autoencoder (VAE)](https://en.wikipedia.org/wiki/Variational_autoencoder) with [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset, and expands on what we learned in [Getting started with JAX](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html). Along the way, you'll learn more about how JAX's [JIT compilation](https://jax.readthedocs.io/en/latest/jit-compilation.html#jit-compilation) (`jax.jit`) actually works, and what this means for [debugging](https://jax.readthedocs.io/en/latest/debugging/index.html) [JAX programs](https://jax.readthedocs.io/en/latest/debugging.html), as we learn how to identify what can go wrong during model training.\n",
     "\n",
-    "If you are new to JAX for AI, check out the [first tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which explains how to build a simple neural netwwork with Flax and Optax, and JAX's key features, including the NumPy-style interface with `jax.numpy`, JAX transformations for JIT compilation with `jax.jit`, automatic vectorization with `jax.vmap`, and automatic differentiation with `jax.grad`."
+    "If you are new to JAX for AI, check out the [first tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which explains how to build a simple neural netwwork with Flax and Optax, and JAX's key features, including the NumPy-style interface with `jax.numpy`, JAX transformations for JIT compilation with `jax.jit`, automatic vectorization with `jax.vmap`, and automatic differentiation with `jax.grad`."
    ]
   },
   {
@@ -23,7 +23,7 @@
    "source": [
     "## Loading the data\n",
     "\n",
-    "As [before](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), this example uses the well-known, small and self-contained [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset:"
+    "As [before](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), this example uses the well-known, small and self-contained [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset:"
    ]
   },
   {
@@ -64,7 +64,7 @@
     "id": "2_Q16JRyrW7V"
    },
    "source": [
-    "The dataset comprises 1800 images of hand-written digits, each represented by an `8x8` pixel grid, and their corresponding labels. For visualization of this data, refer to [loading the data](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html#loading-the-data) in the previous tutorial."
+    "The dataset comprises 1800 images of hand-written digits, each represented by an `8x8` pixel grid, and their corresponding labels. For visualization of this data, refer to [loading the data](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html#loading-the-data) in the previous tutorial."
    ]
   },
   {
@@ -75,7 +75,7 @@
    "source": [
     "## Defining the VAE with Flax\n",
     "\n",
-    "[Previously](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), we learned how to use [Flax NNX](http://flax.readthedocs.io) to create a simple [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) neural network trained for classification with an architecture that looked roughly like this:"
+    "[Previously](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), we learned how to use [Flax NNX](http://flax.readthedocs.io) to create a simple [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) neural network trained for classification with an architecture that looked roughly like this:"
    ]
   },
   {

docs/source/digits_vae.md

@@ -17,15 +17,15 @@ kernelspec:
 
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/digits_vae.ipynb)
 
-This tutorial explores a simplified version of a generative model called [Variational Autoencoder (VAE)](https://en.wikipedia.org/wiki/Variational_autoencoder) with [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset, and expands on what we learned in [Getting started with JAX](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html). Along the way, you'll learn more about how JAX's [JIT compilation](https://jax.readthedocs.io/en/latest/jit-compilation.html#jit-compilation) (`jax.jit`) actually works, and what this means for [debugging](https://jax.readthedocs.io/en/latest/debugging/index.html) [JAX programs](https://jax.readthedocs.io/en/latest/debugging.html), as we learn how to identify what can go wrong during model training.
+This tutorial explores a simplified version of a generative model called [Variational Autoencoder (VAE)](https://en.wikipedia.org/wiki/Variational_autoencoder) with [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset, and expands on what we learned in [Getting started with JAX](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html). Along the way, you'll learn more about how JAX's [JIT compilation](https://jax.readthedocs.io/en/latest/jit-compilation.html#jit-compilation) (`jax.jit`) actually works, and what this means for [debugging](https://jax.readthedocs.io/en/latest/debugging/index.html) [JAX programs](https://jax.readthedocs.io/en/latest/debugging.html), as we learn how to identify what can go wrong during model training.
 
-If you are new to JAX for AI, check out the [first tutorial](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), which explains how to build a simple neural netwwork with Flax and Optax, and JAX's key features, including the NumPy-style interface with `jax.numpy`, JAX transformations for JIT compilation with `jax.jit`, automatic vectorization with `jax.vmap`, and automatic differentiation with `jax.grad`.
+If you are new to JAX for AI, check out the [first tutorial](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), which explains how to build a simple neural netwwork with Flax and Optax, and JAX's key features, including the NumPy-style interface with `jax.numpy`, JAX transformations for JIT compilation with `jax.jit`, automatic vectorization with `jax.vmap`, and automatic differentiation with `jax.grad`.
 
 +++ {"id": "k19povzxp7hS"}
 
 ## Loading the data
 
-As [before](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), this example uses the well-known, small and self-contained [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset:
+As [before](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), this example uses the well-known, small and self-contained [scikit-learn `digits`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) dataset:
 
 ```{code-cell}
 :id: aIwDAfS6PtFh
@@ -47,13 +47,13 @@ print(f"{images_test.shape=}")
 
 +++ {"id": "2_Q16JRyrW7V"}
 
-The dataset comprises 1800 images of hand-written digits, each represented by an `8x8` pixel grid, and their corresponding labels. For visualization of this data, refer to [loading the data](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html#loading-the-data) in the previous tutorial.
+The dataset comprises 1800 images of hand-written digits, each represented by an `8x8` pixel grid, and their corresponding labels. For visualization of this data, refer to [loading the data](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html#loading-the-data) in the previous tutorial.
 
 +++ {"id": "Z9TPYqipPyBp"}
 
 ## Defining the VAE with Flax
 
-[Previously](https://jax-ai-stack.readthedocs.io/en/latest/getting_started_with_jax_for_AI.html), we learned how to use [Flax NNX](http://flax.readthedocs.io) to create a simple [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) neural network trained for classification with an architecture that looked roughly like this:
+[Previously](https://jax-ai-stack.readthedocs.io/en/latest/neural_net_basics.html), we learned how to use [Flax NNX](http://flax.readthedocs.io) to create a simple [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) neural network trained for classification with an architecture that looked roughly like this:
 
 ```{code-cell}
 :id: HNlg-ydpr5yH

docs/source/getting_started.md

@@ -20,7 +20,7 @@ After working through this content, you may wish to visit the [JAX documentation
 ```{toctree}
 :maxdepth: 1
 
-getting_started_with_jax_for_AI
+neural_net_basics
 digits_vae
 digits_diffusion_model
 ```

docs/source/getting_started_with_jax_for_AI.ipynb renamed to docs/source/neural_net_basics.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Part 1: JAX neural net basics\n",
     "\n",
-    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/getting_started_with_jax_for_AI.ipynb)"
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/neural_net_basics.ipynb)"
    ]
   },
   {

docs/source/getting_started_with_jax_for_AI.md renamed to docs/source/neural_net_basics.md

@@ -15,7 +15,7 @@ kernelspec:
 
 # Part 1: JAX neural net basics
 
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/getting_started_with_jax_for_AI.ipynb)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/neural_net_basics.ipynb)
 
 +++ {"id": "z7sAr0sderhh"}