fix: docs: removing unnecessary "what is next" cards.

cnpem · May 22, 2024 · 3260290 · 3260290
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diff --git a/apps/docs/content/docs/components/inference.mdx b/apps/docs/content/docs/components/inference.mdx
@@ -23,9 +23,4 @@ Adds a 0-valued frame around the input image to ensure that boundary effects to
 
 ## 4 - Patch size
 
-Similar to Volume Padding, this parameter controls the amount of overlap between patches sampled over the target image for inference. Lower values increase classification speed at the cost of edge artifacts. In other words, it determines how much edge will be thrown away when making the inference. We throw it away because of an edge effect on each patch inside the image.
-
-
-<Cards>
-  <Card title="Network" href="/docs/components/network" />
-</Cards>
+Similar to Volume Padding, this parameter controls the amount of overlap between patches sampled over the target image for inference. Lower values increase classification speed at the cost of edge artifacts. In other words, it determines how much edge will be thrown away when making the inference. We throw it away because of an edge effect on each patch inside the image.
diff --git a/apps/docs/content/docs/components/network.mdx b/apps/docs/content/docs/components/network.mdx
@@ -193,10 +193,3 @@ In deepsirius context, fine tuning is the process of retraining a trained networ
 **Behavior when `drop_classifier` is not 'selected':**
 
 - **Full Model Restoration**: If `drop_classifier` is not set to 'Yes', all variables, including those for the classifier and optimizer, are restored. This is the default behavior when you want to load the entire model as it was originally trained, without any modifications.
-
-## What is Next?
-
-<Cards>
-  <Card title="Dataset" href="/docs/components/dataset" />
-  <Card title="Inference" href="/docs/components/inference" />
-</Cards>
diff --git a/apps/docs/content/docs/components/workspace.mdx b/apps/docs/content/docs/components/workspace.mdx
@@ -290,10 +290,4 @@ following possible visualizations:
   a secondary process that starts a tensorboard view of the logs of the training
   process. The tensorboard view is a tool that helps the user to monitor the
   training process of the deep learning model. The tensorboard view is available
-  for the training and finetuning nodes.
-
-## What is Next?
-
-<Cards>
-  <Card title="Dataset" href="/docs/components/dataset" />
-</Cards>
+  for the training and finetuning nodes.