Merge pull request #559 from GoogleCloudPlatform/dev-ghi-issue01

updated the definition of baseline model in keras_feat_eng.ipynb
GoogleCloudPlatform · Dec 13, 2024 · 20f7413 · 20f7413
2 parents d5f3728 + d40cee6
commit 20f7413
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diff --git a/notebooks/feature_engineering/labs/4_keras_adv_feat_eng.ipynb b/notebooks/feature_engineering/labs/4_keras_adv_feat_eng.ipynb
@@ -164,7 +164,7 @@
    "source": [
     "## Create a Baseline DNN Model in Keras\n",
     "\n",
-    "Now let's build the Deep Neural Network (DNN) model in Keras using the functional API. Unlike the sequential API, we will need to specify the input and hidden layers.  Note that we are creating a linear regression baseline model with no feature engineering. Recall that a baseline model is a solution to a problem without applying any machine learning techniques."
+    "Now let's build the Deep Neural Network (DNN) model in Keras using the functional API. Unlike the sequential API, we will need to specify the input and hidden layers.  Note that we are creating a linear regression baseline model with no feature engineering. A baseline model is a simple, basic model that acts as a reference point for evaluating the performance of more complex models."
    ]
   },
   {
@@ -618,15 +618,15 @@
  ],
  "metadata": {
   "environment": {
-   "kernel": "conda-base-py",
-   "name": "workbench-notebooks.m121",
+   "kernel": "python3",
+   "name": "tf2-gpu.2-17.m126",
    "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m121"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf2-gpu.2-17:m126"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) (Local)",
+   "display_name": "Python 3 (Local)",
    "language": "python",
-   "name": "conda-base-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -638,7 +638,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/feature_engineering/solutions/4_keras_adv_feat_eng.ipynb b/notebooks/feature_engineering/solutions/4_keras_adv_feat_eng.ipynb
@@ -163,7 +163,7 @@
    "source": [
     "## Create a Baseline DNN Model in Keras\n",
     "\n",
-    "Now let's build the Deep Neural Network (DNN) model in Keras using the functional API. Unlike the sequential API, we will need to specify the input and hidden layers.  Note that we are creating a linear regression baseline model with no feature engineering. Recall that a baseline model is a solution to a problem without applying any machine learning techniques."
+    "Now let's build the Deep Neural Network (DNN) model in Keras using the functional API. Unlike the sequential API, we will need to specify the input and hidden layers.  Note that we are creating a linear regression baseline model with no feature engineering. A baseline model is a simple, basic model that acts as a reference point for evaluating the performance of more complex models."
    ]
   },
   {
@@ -649,15 +649,15 @@
  ],
  "metadata": {
   "environment": {
-   "kernel": "conda-base-py",
-   "name": "workbench-notebooks.m121",
+   "kernel": "python3",
+   "name": "tf2-gpu.2-17.m126",
    "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m121"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf2-gpu.2-17:m126"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) (Local)",
+   "display_name": "Python 3 (Local)",
    "language": "python",
-   "name": "conda-base-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -669,7 +669,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,