diff --git a/content/pytorch/concepts/tensor-operations/tensor-operations.md b/content/pytorch/concepts/tensor-operations/tensor-operations.md
index 295ededa1ed..2a57fbb337e 100644
--- a/content/pytorch/concepts/tensor-operations/tensor-operations.md
+++ b/content/pytorch/concepts/tensor-operations/tensor-operations.md
@@ -9,6 +9,7 @@ Tags:
   - 'Arrays'
   - 'Deep Learning'
   - 'TensorFlow'
+  - 'PyTorch'
 CatalogContent:
   - 'intro-to-py-torch-and-neural-networks'
   - 'py-torch-for-classification'
diff --git a/content/pytorch/pytorch.md b/content/pytorch/pytorch.md
index bfb866f0f64..44953c75be5 100644
--- a/content/pytorch/pytorch.md
+++ b/content/pytorch/pytorch.md
@@ -17,7 +17,7 @@ PyTorch is widely adopted in both academia and industry, competing closely with
 | Feature                           | Description                                                                                                                                                                                                                         |
 | --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | Dynamic Computational Graphs      | PyTorch uses dynamic computation graphs, allowing for changes to network architecture during runtime.                                                                                                                               |
-| Tensor Computation                | PyTorch offers a comprehensive library for tensor operations, similar to [NumPy](https://www.codecademy.com/resources/docs/numpy), but with GPU acceleration.                                                                       |
+| Tensor Computations               | PyTorch offers a comprehensive library for tensor operations, similar to [NumPy](https://www.codecademy.com/resources/docs/numpy), but with GPU acceleration.                                                                       |
 | Deep Learning Support             | It includes modules for constructing deep learning models, such as CNNs, RNNs, and transformers.                                                                                                                                    |
 | Autograd                          | PyTorch's automatic differentiation library, Autograd, enables easy computation of gradients, essential for training neural networks.                                                                                               |
 | Rich Ecosystem                    | PyTorch has a thriving ecosystem with numerous libraries and tools, such as TorchVision for computer vision, TorchText for natural language processing, etc.                                                                        |