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ilabcode · Sep 13, 2024 · bfce6c9 · bfce6c9
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diff --git a/docs/source/learn.md b/docs/source/learn.md
@@ -67,7 +67,7 @@ How the generative model of the Hierarchical Gaussian filter can be turned into
 :::{grid-item-card}  Creating and manipulating networks of probabilistic nodes
 :link: probabilistic_networks
 :link-type: ref
-:img-top: ./images/graph_networks.svg
+:img-top: ./images/graph_network.svg
 
 How to create and manipulate a network of probabilistic nodes for reinforcement learning? Working at the intersection of graphs, neural networks and probabilistic frameworks.
 :::

diff --git a/docs/source/notebooks/0.2-Creating_networks.ipynb b/docs/source/notebooks/0.2-Creating_networks.ipynb
@@ -132,7 +132,7 @@
     "\n",
     "This list describes the sequence of function-to-nodes instructions that are executed during the inference and update processes.\n",
     "\n",
-    "![graph_networks](../images/graph_networks.svg)\n",
+    "![graph_network](../images/graph_network.svg)\n",
     "\n",
     "```{tip} Compatibility with JAX transformations\n",
     "One of the advantages of reasoning this way is that it dissociates variables that are transparent to the JAX framework and can be expressed as \"PyTress\" from variables that should be filtered before transformations. The variable `attributes` ($\\theta$) is typically expressed as a PyTree while the other variables that contain parametrized functions are filtered. See [the documattion](https://jax.readthedocs.io/en/latest/notebooks/thinking_in_jax.html#jit-mechanics-tracing-and-static-variables) for further details on JAX transformations.\n",