doc fixes (openvinotoolkit#3626)

Co-authored-by: Nikolay Tyukaev <[email protected]>
# Conflicts:
#	docs/IE_DG/network_state_intro.md

docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md

@@ -28,7 +28,7 @@ The example below demonstrates an exemplary model that requires previously creat
 @snippet onnx_custom_op/onnx_custom_op.cpp onnx_custom_op:model
 
 
-For a reference on how to create a graph with nGraph operations, visit [nGraph tutorial](../nGraphTutorial.md).
+For a reference on how to create a graph with nGraph operations, visit [Custom nGraph Operations](AddingNGraphOps.md).
 For a complete list of predefined nGraph operators, visit [available operations sets](../../ops/opset.md).
 
 If operator is no longer needed, it can be unregistered by calling `unregister_operator`. The function takes three arguments `op_type`, `version`, and `domain`.

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md

@@ -110,7 +110,7 @@ where:
 
 > **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).
 
-OpenVINO&trade; toolkit provides a demo that uses YOLOv3 model. For more information, refer to [Object Detection YOLO* V3 Demo, Async API Performance Showcase](@ref omz_demos_object_detection_demo_yolov3_async_README).
+OpenVINO&trade; toolkit provides a demo that uses YOLOv3 model. For more information, refer to [Object Detection C++ Demo](@ref omz_demos_object_detection_demo_ssd_async_README).
 
 ## Convert YOLOv1 and YOLOv2 Models to the IR
 

docs/doxygen/ie_docs.xml

@@ -98,6 +98,7 @@ limitations under the License.
                 <tab type="user" title="Atan-1" url="@ref openvino_docs_ops_arithmetic_Atan_1"/>
                 <tab type="user" title="Atanh-3" url="@ref openvino_docs_ops_arithmetic_Atanh_3"/>
                 <tab type="user" title="AvgPool-1" url="@ref openvino_docs_ops_pooling_AvgPool_1"/>
+                <tab type="user" title="BatchNormInference-1" url="@ref openvino_docs_ops_normalization_BatchNormInference_1"/>
                 <tab type="user" title="BatchNormInference-5" url="@ref openvino_docs_ops_normalization_BatchNormInference_5"/>
                 <tab type="user" title="BatchToSpace-2" url="@ref openvino_docs_ops_movement_BatchToSpace_2"/>
                 <tab type="user" title="BinaryConvolution-1" url="@ref openvino_docs_ops_convolution_BinaryConvolution_1"/>

docs/install_guides/installing-openvino-windows.md

@@ -473,7 +473,7 @@ To learn more about converting deep learning models, go to:
 - [Intel Distribution of OpenVINO Toolkit home page](https://software.intel.com/en-us/openvino-toolkit)
 - [Intel Distribution of OpenVINO Toolkit documentation](https://software.intel.com/en-us/openvino-toolkit/documentation/featured)
 - [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
-- [Introduction to Inference Engine](inference_engine_intro.md)
+- [Introduction to Inference Engine](../IE_DG/inference_engine_intro.md)
 - [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 - [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)

inference-engine/ie_bridges/python/sample/ngraph_function_creation_sample/README.md

@@ -13,7 +13,7 @@ When the inference is done, the application outputs inference results to the sta
 
 > **NOTE**: This sample supports models with FP32 weights only.
 
-The `lenet.bin` weights file was generated by the [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) 
+The `lenet.bin` weights file was generated by the [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) 
 tool from the public LeNet model with the `--input_shape [64,1,28,28]` parameter specified. 
 The original model is available in the [Caffe* repository](https://github.com/BVLC/caffe/tree/master/examples/mnist) on GitHub\*.
 
@@ -69,4 +69,4 @@ By default, the application outputs top-1 inference result for each inference re
 
 ## See Also
 
-* [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
+* [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)