Add z-threshold part for network editor tutorial (#922)

Co-authored-by: reuvenp <[email protected]>

tutorials/notebooks/example_keras_network_editor.ipynb

@@ -228,6 +228,46 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Edit Z-Threshold for Activation Quantization\n",
+        "\n",
+        "In the context of model quantization, the Z-Threshold helps in handling outliers in the activation data. Outliers in the data can hurt the quantization process, leading to less efficient and potentially less accurate models.\n",
+        "\n",
+        "The Z-Threshold is used to set a boundary, beyond which extreme values in the activation data are considered outliers and are not used to determine the quantization parameters. This approach effectively filters out extreme values, ensuring a more robust and representative quantization.\n",
+        "\n",
+        "Adjusting the Z-Threshold can be particularly useful during the debugging and optimization of model quantization. By tweaking this parameter, you can fine-tune the balance between model accuracy and robustness against outliers in your specific use case.\n",
+        "\n",
+        "A higher Z-Threshold means more data is considered during quantization, including some outliers, which might be necessary for certain models or datasets.\n",
+        "\n",
+        "The following code demonstrates how you can customize the Z-Threshold for a specific layer type (Conv2D) in a Keras model using MCT's network editor functionality. This feature allows you to set different Z-Threshold values for different layers. By default, all layers use threshold of infinity (thus, no outlier-removal occurs)."
+      ],
+      "metadata": {
+        "id": "2TqXTB48jKHx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "z_threshold_target = 5\n",
+        "edit_rules_list = [\n",
+        "    mct.network_editor.EditRule(\n",
+        "        filter=mct.network_editor.NodeTypeFilter(Conv2D),\n",
+        "        action=mct.network_editor.ChangeCandidatesActivationQuantConfigAttr(z_threshold=z_threshold_target)\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "debug_config = mct.DebugConfig(network_editor=edit_rules_list)\n",
+        "core_config = mct.core.CoreConfig(debug_config=debug_config)\n",
+        "quantized_model = quantize_keras_mct(model, representative_data_gen, core_config)"
+      ],
+      "metadata": {
+        "id": "VBRfQqZVjN3J"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "source": [