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DeepTrackAI · Apr 15, 2024 · 1be74b4 · 1be74b4
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diff --git a/tutorials/getting-started/GS111_first_model.ipynb b/tutorials/getting-started/GS111_first_model.ipynb
@@ -4,16 +4,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Training your first model\n",
+    "# Training Your First Model\n",
     "\n",
-    "In this section, you'll train a simple feedforward neural network on MNIST using Deeplay. You'll define the model, loss function, optimizer, and training loop, and then train the model on the dataset."
+    "In this section, you'll train a simple feedforward neural network on the MNIST using Deeplay. You'll define the model, loss function, optimizer, and training loop, and then train the model on the dataset."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Loading the dataset\n",
+    "## Loading the Dataset\n",
     "\n",
     "Data can come from anywhere. MNIST is most easily loaded using the torchvision library, which provides a simple interface for downloading and loading common datasets. Here, you'll load the MNIST dataset and create a PyTorch DataLoader to iterate over the dataset in batches."
    ]

diff --git a/tutorials/getting-started/GS121_configure.ipynb b/tutorials/getting-started/GS121_configure.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Working with Deeplay Modules\n",
+    "# Configuring Deeplay Objects\n",
     "\n",
     "In this section, you'll explore the difference between Deeplay and PyTorch modules. You'll learn how to create and build Deeplay modules as well as how to configure their properties."
    ]