Fix typos in docs (#186)

docs/Credits.md

@@ -1,10 +1,10 @@
-Some pre-trained models provided by other repository are used in some packages.
+Some pre-trained models provided by other repositories are used in some packages.
 
  - tensorrt_yolo3 <br>
 The pre-trained models are provided in the following repository. The trained file is automatically downloaded when you build. <br>
 https://github.com/lewes6369/TensorRT-Yolov3 <br>
 \[Original URL] <br>
-Tranined file (416) : https://drive.google.com/drive/folders/18OxNcRrDrCUmoAMgngJlhEglQ1Hqk_NJ
+Trained file (416) : https://drive.google.com/drive/folders/18OxNcRrDrCUmoAMgngJlhEglQ1Hqk_NJ
 
 - traffic_light_fine_detector <br>
 A trained model in this package is based on the following .weights file and was fine-tuned with darknet by Tier IV. <br>
@@ -14,7 +14,7 @@ After fine-tuning, the trained model is converted to ONNX file with the followin
 https://github.com/tier4/Pilot.Auto/blob/master/src/perception/traffic_light_recognition/traffic_light_fine_detector/scripts/yolov3_to_onnx.py <br>
 
 - lidar_apollo_instance_segmentation <br>
-This package makes use of three pre-trained models provided by apollo. These files are automatically downloaded when you build. <br>
+This package makes use of three pre-trained models provided by Apollo. These files are automatically downloaded when you build. <br>
 \[Original URL] <br>
 VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel <br>
 HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel <br>

docs/porting-to-ROS2.md

@@ -151,7 +151,7 @@ In ROS2, you should define semantically meaningful wrappers around primitive (nu
 
 
 ### Changing the namespaces and header files for generated message types
-If you follow the migration guide and change the included headers to have an extra `/msg` in the path and convert to `snake_case`, you might get a cryptic error. Turns out _two_ files are being generated: One for C types (`.h` headers) and and one for CPP types (`.hpp` headers). So don't forget to change `.h` to `.hpp` too. Also, don't forget to insert an additional `::msg` between the package namespace and the class name.
+If you follow the migration guide and change the included headers to have an extra `/msg` in the path and convert to `snake_case`, you might get a cryptic error. Turns out _two_ files are being generated: One for C types (`.h` headers) and one for CPP types (`.hpp` headers). So don't forget to change `.h` to `.hpp` too. Also, don't forget to insert an additional `::msg` between the package namespace and the class name.
 
 A tip: Sublime Text has a handy "Case Conversion" package for converting to snake case.
 
@@ -325,7 +325,7 @@ DECLARE_MPC_PARAM(mpc_param_, prediction_horizon, 50);
 set_param_res_ = add_on_set_parameters_callback(std::bind(&MPCFollower::paramCallback, this, _1));
 ```
 
-Inside the callback, you have to manually update each parameter for which you want to react to changes from the outside. You can (inadvertently) declare more parameters than you react to.
+Inside the callback, you have to manually update each parameter for which you want to react to change from the outside. You can (inadvertently) declare more parameters than you react to.
 
 ```c++
   rcl_interfaces::msg::SetParametersResult result;