Update IntroToCNNs.md

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@ -63,7 +63,8 @@ The convolutional layer is the core building block of a CNN. The layer's paramet
#### Input Shape
The dimensions of the input image, including the number of channels (e.g., 3 for RGB images & 1 for Grayscale images).
<div style="display: flex; justify-content: space-around; align-items: center;">
<div>
<p align='left'>
<table>
<caption>1 and 0</caption>
<tbody>
@ -76,6 +77,8 @@ The dimensions of the input image, including the number of channels (e.g., 3 for
<tr> <td>1</td><td>0</td><td>1</td><td>1</td><td>1</td> </tr>
</tbody>
</table>
</p>
<p align='right'>
<table>
<caption>9</caption>
<tbody>
@ -88,6 +91,7 @@ The dimensions of the input image, including the number of channels (e.g., 3 for
<tr> <td>0</td><td>0</td><td>0</td><td>1</td><td>0</td> </tr>
</tbody>
</table>
</p>
</div>
- The input matrix represents a simplified binary image of handwritten digits,
@ -450,4 +454,4 @@ dropout_output = cnn_model.dropout(flattened_output, dropout_rate=0.3)
print("\nDropout Output:\n", dropout_output)
```
Feel free to play around with the parameters!
Feel free to play around with the parameters!