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