diff --git a/Resources/Books/Intro-Deeplearning_SimonJDPrince/index.qmd b/Resources/Books/Intro-Deeplearning_SimonJDPrince.qmd
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diff --git a/Resources/Guides/CHTC/index.qmd b/Resources/Guides/CHTC.qmd
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@@ -7,6 +7,7 @@ image: "../../../images/CHTC.png"
 categories:
   - Compute
   - GPU
+  - Guide
 ---
 
 # The Center for High Throughput Computing (CHTC) - Your Gateway to Efficient Computing
diff --git a/Resources/Guides/github-desktop/index.qmd b/Resources/Guides/Github-desktop.qmd
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@@ -8,6 +8,7 @@ categories:
   - Version control
   - Reproducibility
   - Git/GitHub
+  - Guide
 ---
 
 Navigating the world of version control systems like Git can initially feel daunting, especially for those new to programming or collaborative software development projects. However, with the right tools and guidance, anyone can quickly grasp the essentials and begin leveraging the power of Git for efficient project management and collaboration. While Git commands can be run via a Unix shell, there are alternatives which are more friendly for beginners. In this guide, we'll explore why GitHub Desktop serves as an ideal gateway to Git, along with a step-by-step walkthrough on essential Git terminology, setup procedures, tracking changes, collaboration workflows, and even managing Kaggle notebooks seamlessly. Whether you're embarking on your first coding adventure or seeking to streamline your team's development process, this guide aims to demystify Git and empower you with practical knowledge to navigate the Git landscape with confidence.
diff --git a/Resources/Guides/Model-sharing/index.qmd b/Resources/Guides/Model-sharing.qmd
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@@ -8,6 +8,7 @@ categories:
   - Model sharing
   - Hugging Face
   - Reproducibility
+  - Guide
 description: Model Sharing Via Hugging Face's Model Hub
 ---
 
diff --git a/Resources/Guides/OOD-detection/index.qmd b/Resources/Guides/OOD-detection.qmd
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 categories:
   - OOD detection
   - Trustworthy ML
+  - Guide
 ---
 ## Out-of-Distribution Detection
 The below tutorial from Sharon Li, an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, introduces a pervasive problem faced by many machine learning systems deployed in the wild — out-of-distribution data.