diff --git a/tutorials/02-intermediate/language_model/main.py b/tutorials/02-intermediate/language_model/main.py
index 3c03db0..ef135bb 100644
--- a/tutorials/02-intermediate/language_model/main.py
+++ b/tutorials/02-intermediate/language_model/main.py
@@ -76,7 +76,7 @@ for epoch in range(num_epochs):
         loss = criterion(outputs, targets.reshape(-1))
         
         # Backward and optimize
-        model.zero_grad()
+        optimizer.zero_grad()
         loss.backward()
         clip_grad_norm_(model.parameters(), 0.5)
         optimizer.step()
diff --git a/tutorials/03-advanced/generative_adversarial_network/main.py b/tutorials/03-advanced/generative_adversarial_network/main.py
index 34f4127..c2062cf 100644
--- a/tutorials/03-advanced/generative_adversarial_network/main.py
+++ b/tutorials/03-advanced/generative_adversarial_network/main.py
@@ -22,10 +22,14 @@ if not os.path.exists(sample_dir):
     os.makedirs(sample_dir)
 
 # Image processing
+# transform = transforms.Compose([
+#                 transforms.ToTensor(),
+#                 transforms.Normalize(mean=(0.5, 0.5, 0.5),   # 3 for RGB channels
+#                                      std=(0.5, 0.5, 0.5))])
 transform = transforms.Compose([
                 transforms.ToTensor(),
-                transforms.Normalize(mean=(0.5, 0.5, 0.5),   # 3 for RGB channels
-                                     std=(0.5, 0.5, 0.5))])
+                transforms.Normalize(mean=[0.5],   # 1 for greyscale channels
+                                     std=[0.5])])
 
 # MNIST dataset
 mnist = torchvision.datasets.MNIST(root='../../data/',