diff --git a/MONAICore/3. End-To-End Workflow with MONAI - Solution.ipynb b/MONAICore/3. End-To-End Workflow with MONAI - Solution.ipynb
index 76169cc..d152c21 100644
--- a/MONAICore/3. End-To-End Workflow with MONAI - Solution.ipynb	
+++ b/MONAICore/3. End-To-End Workflow with MONAI - Solution.ipynb	
@@ -450,7 +450,8 @@
     "    print(f\"epoch {epoch + 1}/{epoch_num}\")\n",
     "\n",
     "    epoch_loss = 0\n",
-    "    step = 1\n",
+    "    step = 0\n",
+    "    total_items = 0\n",
     "\n",
     "    steps_per_epoch = len(train_ds) // train_loader.batch_size\n",
     "\n",
@@ -478,11 +479,12 @@
     "        # and its internal optimisation strategy\n",
     "        optimizer.step()\n",
     "\n",
-    "        epoch_loss += loss.item()\n",
-    "        print(f\"{step}/{len(train_ds) // train_loader.batch_size + 1}, training_loss: {loss.item():.4f}\")\n",
+    "        epoch_loss += loss.item() * outputs.shape[0]\n",
+    "        total_items += outputs.shape[0]\n",
+    "        print(f\"{step + 1}/{len(train_ds) // train_loader.batch_size + 1}, training_loss: {loss.item():.4f}\")\n",
     "        step += 1\n",
     "\n",
-    "    epoch_loss /= step\n",
+    "    epoch_loss /= total_items\n",
     "    epoch_loss_values.append(epoch_loss)\n",
     "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
     "\n",
diff --git a/MONAICore/3. End-To-End Workflow with MONAI.ipynb b/MONAICore/3. End-To-End Workflow with MONAI.ipynb
index 01f6529..f37a979 100644
--- a/MONAICore/3. End-To-End Workflow with MONAI.ipynb	
+++ b/MONAICore/3. End-To-End Workflow with MONAI.ipynb	
@@ -450,7 +450,8 @@
     "    print(f\"epoch {epoch + 1}/{epoch_num}\")\n",
     "\n",
     "    epoch_loss = 0\n",
-    "    step = 1\n",
+    "    step = 0\n",
+    "    total_items = 0\n",
     "\n",
     "    steps_per_epoch = len(train_ds) // train_loader.batch_size\n",
     "\n",
@@ -478,11 +479,12 @@
     "        # and its internal optimisation strategy\n",
     "        optimizer.step()\n",
     "\n",
-    "        epoch_loss += loss.item()\n",
-    "        print(f\"{step}/{len(train_ds) // train_loader.batch_size + 1}, training_loss: {loss.item():.4f}\")\n",
+    "        epoch_loss += loss.item() * outputs.shape[0]\n",
+    "        total_items += outputs.shape[0]\n",
+    "        print(f\"{step + 1}/{len(train_ds) // train_loader.batch_size + 1}, training_loss: {loss.item():.4f}\")\n",
     "        step += 1\n",
     "\n",
-    "    epoch_loss /= step\n",
+    "    epoch_loss /= total_items\n",
     "    epoch_loss_values.append(epoch_loss)\n",
     "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
     "\n",
diff --git a/MONAICore/MONAI Bundle, MONAI Model Zoo, and Image Visualization.ipynb b/MONAICore/MONAI Bundle, MONAI Model Zoo, and Image Visualization.ipynb
index c8efa0f..8e3daf3 100644
--- a/MONAICore/MONAI Bundle, MONAI Model Zoo, and Image Visualization.ipynb	
+++ b/MONAICore/MONAI Bundle, MONAI Model Zoo, and Image Visualization.ipynb	
@@ -109,7 +109,11 @@
    "source": [
     "## MONAI Bundle\n",
     "\n",
-    "The objective of the MONAI bundle is to define a packaged model which includes the critical information necessary to allow users and programs to understand how the model is used and what purpose it is for. A bundle includes the stored weights of a single network as a pickled state dictionary plus optionally a Torchscript object and/or an ONNX object. Additional JSON files are included to store metadata about the model, information for constructing training, inference, and post-processing transform sequences, plain-text description, legal information, and other data the model creator wishes to include."
+    "MONAI bundles define a packaged model which includes the critical information necessary to allow users and programs to understand how the model is used and what purpose it is for. \n",
+    "\n",
+    "* Stored weights of a single network as a pickled state dictionary are included, plus optional Torchscript and/or ONNX object. \n",
+    "\n",
+    "* Additional JSON files store metadata about the model, information for constructing training, inference, and post-processing transform sequences, plain-text description, legal information, and other data the model creator wishes to include."
    ]
   },
   {
@@ -210,7 +214,7 @@
     }
    },
    "source": [
-    "or alternatively, in `YAML` format (`demo_config.yaml`):\n",
+    "Alternatively, in `YAML` format (`demo_config.yaml`):\n",
     "\n",
     "```yaml\n",
     "demo_net:\n",