Add note to recommend tuning hyperparameters for downstream applications

examples/cell_classification.ipynb

@@ -176,6 +176,14 @@
     "    }"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "beaab7a4-cc13-4e8f-b137-ed18ff7b633c",
+   "metadata": {},
+   "source": [
+    "### Please note that, as usual with deep learning models, we **highly** recommend tuning learning hyperparameters for all fine-tuning applications as this can significantly improve model performance. Example hyperparameters are defined below, but please see the \"hyperparam_optimiz_for_disease_classifier\" script for an example of how to tune hyperparameters for downstream applications."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -187,7 +195,7 @@
     "# max input size\n",
     "max_input_size = 2 ** 11  # 2048\n",
     "\n",
-    "# set training parameters\n",
+    "# set training hyperparameters\n",
     "# max learning rate\n",
     "max_lr = 5e-5\n",
     "# how many pretrained layers to freeze\n",

examples/gene_classification.ipynb

@@ -444,6 +444,13 @@
     "## Fine-Tune With Gene Classification Learning Objective and Quantify Predictive Performance"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Please note that, as usual with deep learning models, we **highly** recommend tuning learning hyperparameters for all fine-tuning applications as this can significantly improve model performance. Example hyperparameters are defined below, but please see the \"hyperparam_optimiz_for_disease_classifier\" script for an example of how to tune hyperparameters for downstream applications."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -454,7 +461,7 @@
     "# max input size\n",
     "max_input_size = 2 ** 11  # 2048\n",
     "\n",
-    "# set training parameters\n",
+    "# set training hyperparameters\n",
     "# max learning rate\n",
     "max_lr = 5e-5\n",
     "# how many pretrained layers to freeze\n",