Fix more typos and broken links (#15331)

Author: abhinaykukkadapu

docs/source/backends-overview.md

@@ -28,7 +28,7 @@ Backends are the bridge between your exported model and the hardware it runs on.
 | [MediaTek](backends-mediatek)                                   | Android             | NPU           | MediaTek SoCs                   |
 | [ARM EthosU](backends-arm-ethos-u)                              | Embedded            | NPU           | ARM MCUs                        |
 | [ARM VGF](backends-arm-vgf)                                     | Android             | NPU           | ARM platforms                   |
-| [OpenVINO](build-run-openvino)                                  | Embedded            | CPU/GPU/NPU   | Intel  SoCs                     |
+| [OpenVINO](build-run-openvino)                                  | Embedded            | CPU/GPU/NPU   | Intel SoCs                      |
 | [NXP](backends-nxp)                                             | Embedded            | NPU           | NXP SoCs                        |
 | [Cadence](backends-cadence)                                     | Embedded            | DSP           | DSP-optimized workloads         |
 | [Samsung Exynos](/backends/samsung/samsung-overview.md)         | Android             | NPU           | Samsung SoCs                    |

docs/source/examples-end-to-end-to-lower-model-to-delegate.md

@@ -19,7 +19,7 @@ There are three flows for delegating a program to a backend:
     is good for reusing lowered modules exported from other flows.
 1. Lower parts of a module according to a partitioner. This is good for
     lowering models that include both lowerable and non-lowerable nodes, and is
-    the most streamlined procecss.
+    the most streamlined process.
 
 ### Flow 1: Lowering the whole module
 

docs/source/getting-started.md

@@ -1,5 +1,5 @@
 # Getting Started with ExecuTorch
-This section is intended to describe the necessary steps to take PyTorch model and run it using ExecuTorch. To use the framework, you will typically need to take the following steps:
+This section is intended to describe the necessary steps to take a PyTorch model and run it using ExecuTorch. To use the framework, you will typically need to take the following steps:
 - Install the ExecuTorch python package and runtime libraries.
 - Export the PyTorch model for the target hardware configuration.
 - Run the model using the ExecuTorch runtime APIs on your development platform.
@@ -77,7 +77,7 @@ Quantization can also be done at this stage to reduce model size and runtime. Qu
 
 After successfully generating a .pte file, it is common to use the Python runtime APIs to validate the model on the development platform. This can be used to evaluate model accuracy before running on-device.
 
-For the MobileNet V2 model from torchvision used in this example, image inputs are expected as a normalized, float32 tensor with a dimensions of (batch, channels, height, width). The output See [torchvision.models.mobilenet_v2](https://pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v2.html) for more information on the input and output tensor format for this model.
+For the MobileNet V2 model from torchvision used in this example, image inputs are expected as a normalized, float32 tensor with a dimensions of (batch, channels, height, width). The output is a tensor containing class logits. See [torchvision.models.mobilenet_v2](https://pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v2.html) for more information on the input and output tensor format for this model.
 
 ```python
 import torch

docs/source/kernel-library-selective-build.md

@@ -61,7 +61,7 @@ gen_selected_ops(
   ROOT_OPS              # comma separated operator names to be selected
   INCLUDE_ALL_OPS       # boolean flag to include all operators
   OPS_FROM_MODEL        # path to a pte file of model to select operators from
-  DTYPE_SELECTIVE_BUILD # boolean flag to enable dtye selection
+  DTYPE_SELECTIVE_BUILD # boolean flag to enable dtype selection
 )
 ```
 

docs/source/using-executorch-android.md

@@ -28,19 +28,10 @@ The AAR artifact contains the Java library for users to integrate with their Jav
   - Optimized kernels
   - Quantized kernels
   - LLaMa-specific Custom ops library.
-- Comes with two ABI variants, arm64-v8a and x86\_64.
+- Comes with two ABI variants, arm64-v8a and x86_64.
 
 The AAR library can be used for generic Android device with arm64-v8a or x86_64 architecture. It can be used across form factors, including phones, tablets, tv boxes, etc, as it does not contain any UI components.
 
-XNNPACK backend
-
-Portable kernels
-Optimized kernels
-Quantized kernels
-LLaMa-specific Custom ops library.
-Comes with two ABI variants, arm64-v8a and x86_64.
-The AAR library can be used for generic Android device with arm64-v8a or x86_64 architecture. It can be used across form factors, including phones, tablets, tv boxes, etc, as it does not contain any UI components.
-
 ## Using AAR from Maven Central
 
 ✅ Recommended for most developers

docs/source/using-executorch-export.md

@@ -35,7 +35,7 @@ Commonly used hardware backends are listed below. For mobile, consider using XNN
 - [XNNPACK (CPU)](backends/xnnpack/xnnpack-overview.md)
 - [Core ML (iOS)](backends/coreml/coreml-overview.md)
 - [Metal Performance Shaders (iOS GPU)](backends/mps/mps-overview.md)
-- [Vulkan (Android GPU)](backends-vulkan.md)
+- [Vulkan (Android GPU)](backends/vulkan/vulkan-overview.md)
 - [Qualcomm NPU](backends-qualcomm.md)
 - [MediaTek NPU](backends-mediatek.md)
 - [Arm Ethos-U NPU](backends-arm-ethos-u.md)