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skills/transfer-learning-adapter/assets/model_architecture.png

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+(Binary file content for model_architecture.png - A placeholder image representing a typical deep learning model architecture suitable for transfer learning.  This could be a simplified ResNet, VGG, or similar. The image should visually depict layers, connections, and the concept of freezing layers for transfer learning.)
+
+(Image data would go here.  A real PNG file would contain binary data defining the image.)
+
+<!--
+Instructions for replacing this placeholder:
+
+1.  This file is a placeholder for a visual representation of a common deep learning model architecture.
+2.  Use a tool like draw.io, Lucidchart, or similar to create a diagram.
+3.  The diagram should clearly show:
+    *   Input layer
+    *   Multiple convolutional layers (or other relevant layer types)
+    *   Pooling layers (if applicable)
+    *   Fully connected layers (if applicable)
+    *   Output layer
+4.  Highlight the layers that are typically frozen during transfer learning (e.g., the earlier convolutional layers).  Use color or shading to differentiate these layers.
+5.  Label the layers clearly.
+6.  Save the diagram as a PNG file.
+7.  Replace the placeholder binary data in this file with the actual PNG data. You can do this by opening the PNG file in a binary editor and copying the data, or by using a scripting language to read and write the binary data.
+
+Example Architecture Considerations:
+
+*   ResNet: Shows residual connections and the concept of blocks.
+*   VGG:  Shows a deep stack of convolutional layers.
+*   MobileNet:  Focuses on efficient architectures.
+
+The goal is to provide a visual aid to users understanding how transfer learning can be applied.
+-->