diff --git a/tutorials/00 - PyTorch Basics/main.py b/tutorials/00 - PyTorch Basics/main.py
index 1bd907b..4c99ea0 100644
--- a/tutorials/00 - PyTorch Basics/main.py	
+++ b/tutorials/00 - PyTorch Basics/main.py	
@@ -79,9 +79,8 @@ print('loss after 1 step optimization: ', loss.data[0])
 
 #======================== Loading data from numpy ========================#
 a = np.array([[1,2], [3,4]])
-b = torch.from_numpy(a)
-print (b)
-
+b = torch.from_numpy(a)      # convert numpy array to torch tensor
+c = b.numpy()                # convert torch tensor to numpy array
 
 
 #===================== Implementing the input pipline =====================#
@@ -113,6 +112,7 @@ for images, labels in train_loader:
     # Your training code will be written here
     pass
 
+
 #===================== Input pipline for custom dataset =====================#
 # You should build custom dataset as below.
 class CustomDataset(data.Dataset):
@@ -123,14 +123,16 @@ class CustomDataset(data.Dataset):
     def __getitem__(self, index):
         # TODO
         # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
-        # 2. Return a data pair (e.g. image and label).
+        # 2. Preprocess the data (e.g. torchvision.Transform).
+        # 3. Return a data pair (e.g. image and label).
         pass
     def __len__(self):
         # You should change 0 to the total size of your dataset.
         return 0 
 
 # Then, you can just use prebuilt torch's data loader. 
-train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+custom_dataset = CustomDataset()
+train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
                                            batch_size=100, 
                                            shuffle=True,
                                            num_workers=2)
@@ -153,6 +155,11 @@ outputs = resnet(images)
 print (outputs.size())   # (10, 100)
 
 
-#============================ Save and load model ============================#
+#============================ Save and load the model ============================#
+# Save and load the entire model.
 torch.save(resnet, 'model.pkl')
-model = torch.load('model.pkl')
\ No newline at end of file
+model = torch.load('model.pkl')
+
+# Save and load only the model parameters(recommended).
+torch.save(resnet.state_dict(), 'params.pkl')
+resnet.load_state_dict(torch.load('params.pkl'))
\ No newline at end of file