Update Project.ipynb

weight and bias readme update

Project.ipynb

@@ -2224,9 +2224,9 @@
     "\n",
     "## Weights and Biases Evaluation\n",
     "\n",
-    "The weights and biases of the MLP model layers were visualized to understand their distributions. The weights in all layers (dense_8, dense_9, dense_10, and dense_11) are not close to zero, indicating they are likely being updated during training and contributing to the model's learning. The weight distributions show a spread around zero, suggesting the model is capturing complex relationships in the data.\n",
+    "The weights and biases of the MLP model layers were visualized to understand their distributions. The weights in all layers (dense, dense_1, dense_2, and dense_3) are not close to zero, indicating they are likely being updated during training and contributing to the model's learning. The weight distributions show a spread around zero, suggesting the model is capturing complex relationships in the data.\n",
     "\n",
-    "The biases in dense_8 and dense_10 introduce a slight positive bias to the activations in subsequent layers, potentially affecting the model's predictions. The biases in dense_9 and dense_11 are centered around zero, with a slight spread towards positive values, introducing a small positive shift in the activations of the next layer.\n",
+    "The biases in dense and dense_2 introduce a slight positive bias to the activations in subsequent layers, potentially affecting the model's predictions. The biases in dense_1 and dense_3 are centered around zero, with a slight spread towards positive values, introducing a small positive shift in the activations of the next layer.\n",
     "\n",
     "The impact of these biases would depend on the network architecture and data. Overall, the model's weights and biases suggest that it is learning effectively from the training data.\n",
     "\n",