Update pages/3_WithExercises.py

pages/3_WithExercises.py

@@ -81,7 +81,7 @@ print("Third column:", column)
 # Modifying elements
 # Changing the first element of the tensor to 10
 tensor[0, 0] = 10
-print("Modified tensor:\n", tensor)
+print("Modified tensor:", tensor)
 '''
     },
     "Exercise 3: Tensor Reshaping and Transposing": {
@@ -94,16 +94,16 @@ tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])
 # Reshaping the tensor
 # Changing the shape of the tensor to (3, 2)
 reshaped_tensor = tensor.view(3, 2)
-print("Reshaped tensor:\n", reshaped_tensor)
+print("Reshaped tensor:", reshaped_tensor)
 
 # Another way to reshape using reshape()
 reshaped_tensor2 = tensor.reshape(-1)  # Flattening the tensor
-print("Reshaped tensor (flattened):\n", reshaped_tensor2)
+print("Reshaped tensor (flattened):", reshaped_tensor2)
 
 # Transposing the tensor
 # Swapping the dimensions of the tensor (transpose rows and columns)
 transposed_tensor = tensor.t()
-print("Transposed tensor:\n", transposed_tensor)
+print("Transposed tensor:", transposed_tensor)
 
 # Using permute for higher-dimensional tensors
 tensor_3d = torch.randn(2, 3, 4)  # Creating a random 3D tensor
@@ -129,7 +129,7 @@ print("Matrix multiplication result:\n", matmul_result)
 tensor3 = torch.tensor([1, 2, 3])
 tensor4 = torch.tensor([[1], [2], [3]])
 broadcast_result = tensor3 + tensor4
-print("Broadcasting result:\n", broadcast_result)
+print("Broadcasting result:", broadcast_result)
 
 # Element-wise functions
 # Applying sine function element-wise
@@ -152,32 +152,32 @@ print("Square root of tensor3:", sqrt_result)
 
 # Creating tensors filled with zeros and ones
 zeros_tensor = torch.zeros(3, 3)
-print("Zeros tensor:\n", zeros_tensor)
+print("Zeros tensor:", zeros_tensor)
 
 ones_tensor = torch.ones(3, 3)
-print("Ones tensor:\n", ones_tensor)
+print("Ones tensor:", ones_tensor)
 
 # Randomly initialized tensors
 # Uniform distribution in the range [0, 1)
 rand_tensor = torch.rand(3, 3)
-print("Uniform random tensor:\n", rand_tensor)
+print("Uniform random tensor:", rand_tensor)
 
 # Normal distribution with mean 0 and variance 1
 randn_tensor = torch.randn(3, 3)
-print("Normal random tensor:\n", randn_tensor)
+print("Normal random tensor:", randn_tensor)
 
 # Random integers in the range [0, 10)
 randint_tensor = torch.randint(0, 10, (3, 3))
-print("Random integer tensor:\n", randint_tensor)
+print("Random integer tensor:", randint_tensor)
 
 # Initializing tensors with specific distributions
 # Normal distribution with custom mean and standard deviation
 normal_tensor = torch.normal(mean=0, std=1, size=(3, 3))
-print("Normal distribution tensor:\n", normal_tensor)
+print("Normal distribution tensor:", normal_tensor)
 
 # Uniform distribution in a custom range [0, 1)
 uniform_tensor = torch.empty(3, 3).uniform_(0, 1)
-print("Uniform distribution tensor:\n", uniform_tensor)
+print("Uniform distribution tensor:", uniform_tensor)
 '''
     },
     "Exercise 6: Tensor Arithmetic Operations": {