Fix documentation typos (#14084)

docs/build/eps.md

@@ -45,8 +45,8 @@ The onnxruntime code will look for the provider shared libraries in the same loc
 ### Prerequisites
 {: .no_toc }
 
-* Install [CUDA](https://developer.nvidia.com/cuda-toolkit) and [cuDNN](https://developer.nvidia.com/cudnn)
-  * The path to the CUDA installation must be provided via the CUDA_PATH environment variable, or the `--cuda_home` parameter
+* Install [CUDA](https://developer.nvidia.com/cuda-toolkit) and [cuDNN](https://developer.nvidia.com/cudnn).
+  * The path to the CUDA installation must be provided via the CUDA_PATH environment variable, or the `--cuda_home` parameter.
   * The path to the cuDNN installation (include the `cuda` folder in the path) must be provided via the cuDNN_PATH environment variable, or `--cudnn_home` parameter. The cuDNN path should contain `bin`, `include` and `lib` directories.
   * The path to the cuDNN bin directory must be added to the PATH environment variable so that cudnn64_8.dll is found.
 

docs/build/inferencing.md

@@ -272,7 +272,7 @@ There are a few options for building for ARM.
 
 *EASY, SLOW, RECOMMENDED*
 
-This method rely on qemu user mode emulation. It allows you to compile using a desktop or cloud VM through instruction level simulation. You'll run the build on x86 CPU and translate every ARM instruction to x86. This is much faster than compiling natively on a low-end ARM device and avoids out-of-memory issues that may be encountered. The resulting ONNX Runtime Python wheel (.whl) file is then deployed to an ARM device where it can be invoked in Python 3 scripts.
+This method relies on qemu user mode emulation. It allows you to compile using a desktop or cloud VM through instruction level simulation. You'll run the build on x86 CPU and translate every ARM instruction to x86. This is much faster than compiling natively on a low-end ARM device and avoids out-of-memory issues that may be encountered. The resulting ONNX Runtime Python wheel (.whl) file is then deployed to an ARM device where it can be invoked in Python 3 scripts.
 
 Here is [an example for Raspberrypi3 and Raspbian](https://github.com/microsoft/onnxruntime/tree/master/dockerfiles/README.md#arm-32v7). Note: this does not work for Raspberrypi 1 or Zero, and if your operating system is different from what the dockerfile uses, it also may not work.
 
@@ -291,9 +291,9 @@ This option is very fast and allows the package to be built in minutes, but is c
     You can use [GCC](https://gcc.gnu.org/) or [Clang](http://clang.llvm.org/). Both work, but instructions here are based on GCC.
 
     In GCC terms:
-    * "build" describes the type of system on which GCC is being configured and compiled
+    * "build" describes the type of system on which GCC is being configured and compiled.
     * "host" describes the type of system on which GCC runs.
-    * "target" to describe the type of system for which GCC produce code
+    * "target" to describe the type of system for which GCC produce code.
 
     When not cross compiling, usually "build" = "host" = "target". When you do cross compile, usually "build" = "host" != "target". For example, you may build GCC on x86_64, then run GCC on x86_64, then generate binaries that target aarch64. In this case,"build" = "host" = x86_64 Linux, target is aarch64 Linux.
 

docs/build/training.md

@@ -110,6 +110,6 @@ The default AMD GPU build requires ROCm software toolkit installed on the system
 
 `.\build.bat --enable_training --use_dnnl`
 
-Add `--build_wheel` to build the ONNX Runtime wheel
+Add `--build_wheel` to build the ONNX Runtime wheel.
 
-This will produce a .whl file in `build/Linux/RelWithDebInfo/dist` for ONNX Runtime Training
+This will produce a .whl file in `build/Linux/RelWithDebInfo/dist` for ONNX Runtime Training.