Release doc updates_port (#14784)

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

@@ -273,4 +273,4 @@ exec_net = ie.load_network(network=net, device_name="CPU")
 result_ie = exec_net.infer(input_data)
 ```
 
-For more information about Python API, refer to the [OpenVINO Runtime Python API](https://docs.openvino.ai/2022.2/api/api_reference.html) guide.
+For more information about Python API, refer to the [OpenVINO Runtime Python API](https://docs.openvino.ai/latest/api/api_reference.html) guide.

docs/home.rst

@@ -43,7 +43,7 @@ A typical workflow with OpenVINO is shown below.
 
          .. image:: _static/images/OV_flow_optimization_hvr.svg
             :alt: link to an optimization guide
-            :target: openvino_docs_optimization_guide_dldt_optimization_guide.html
+            :target: openvino_docs_model_optimization_guide.html
 
       .. container:: hp-flow-arrow
 

docs/install_guides/installing-openvino-from-archive-linux.md

@@ -55,14 +55,6 @@ See the [Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNo
     
 4. Download the `OpenVINO Runtime archive file for your system <https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3/linux/>`_, extract the files, rename the extracted folder and move it to the desired path:
 
-   .. tab:: Ubuntu 18.04
-
-      .. code-block:: sh
-   
-         curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3/linux/l_openvino_toolkit_ubuntu18_2022.3.0.9052.9752fafe8eb_x86_64.tgz --output openvino_2022.3.0.tgz
-         tar -xf openvino_2022.3.0.tgz
-         sudo mv l_openvino_toolkit_ubuntu18_2022.3.0.9052.9752fafe8eb_x86_64 /opt/intel/openvino_2022.3.0
-      
    .. tab:: Ubuntu 20.04
 
       .. code-block:: sh
@@ -71,6 +63,14 @@ See the [Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNo
          tar -xf openvino_2022.3.0.tgz
          sudo mv l_openvino_toolkit_ubuntu20_2022.3.0.9052.9752fafe8eb_x86_64 /opt/intel/openvino_2022.3.0
   
+   .. tab:: Ubuntu 18.04
+
+      .. code-block:: sh
+   
+         curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3/linux/l_openvino_toolkit_ubuntu18_2022.3.0.9052.9752fafe8eb_x86_64.tgz --output openvino_2022.3.0.tgz
+         tar -xf openvino_2022.3.0.tgz
+         sudo mv l_openvino_toolkit_ubuntu18_2022.3.0.9052.9752fafe8eb_x86_64 /opt/intel/openvino_2022.3.0
+
    .. tab:: RHEL 8
 
       .. code-block:: sh

docs/optimization_guide/nncf/introduction.md

@@ -25,8 +25,8 @@ Adding compression to a training pipeline only requires a few lines of code. The
 ### NNCF Quick Start Examples
 See the following Jupyter Notebooks for step-by-step examples showing how to add model compression to a PyTorch or Tensorflow training pipeline with NNCF:
 
-- [Quantization Aware Training with NNCF and PyTorch](https://docs.openvino.ai/2022.2/notebooks/302-pytorch-quantization-aware-training-with-output.html).
+- [Quantization Aware Training with NNCF and PyTorch](https://docs.openvino.ai/latest/notebooks/302-pytorch-quantization-aware-training-with-output.html).
-- [Quantization Aware Training with NNCF and TensorFlow](https://docs.openvino.ai/2022.2/notebooks/305-tensorflow-quantization-aware-training-with-output.html).
+- [Quantization Aware Training with NNCF and TensorFlow](https://docs.openvino.ai/latest/notebooks/305-tensorflow-quantization-aware-training-with-output.html).
 
 ## Installation
 NNCF is open-sourced on [GitHub](https://github.com/openvinotoolkit/nncf) and distributed as a separate package from OpenVINO. It is also available on PyPI. Install it to the same Python environment where PyTorch or TensorFlow is installed.
@@ -82,5 +82,5 @@ Using compression-aware training requires a training pipeline, an annotated data
 - [Quantizing Models Post-training](@ref pot_introduction)
 - [NNCF GitHub repository](https://github.com/openvinotoolkit/nncf)
 - [NNCF FAQ](https://github.com/openvinotoolkit/nncf/blob/develop/docs/FAQ.md)
-- [Quantization Aware Training with NNCF and PyTorch](https://docs.openvino.ai/2022.2/notebooks/302-pytorch-quantization-aware-training-with-output.html)
+- [Quantization Aware Training with NNCF and PyTorch](https://docs.openvino.ai/latest/notebooks/302-pytorch-quantization-aware-training-with-output.html)
-- [Quantization Aware Training with NNCF and TensorFlow](https://docs.openvino.ai/2022.2/notebooks/305-tensorflow-quantization-aware-training-with-output.html)
+- [Quantization Aware Training with NNCF and TensorFlow](https://docs.openvino.ai/latest/notebooks/305-tensorflow-quantization-aware-training-with-output.html)

docs/optimization_guide/nncf/ptq/ptq_introduction.md

@@ -1,4 +1,4 @@
-# Post-training Quantization w/ NNCF (new) {#nncf_ptq_introduction}
+# Post-training Quantization with NNCF (new) {#nncf_ptq_introduction}
 
 @sphinxdirective
 

tools/pot/docs/Introduction.md

@@ -1,4 +1,4 @@
-# Post-training Quantization w/ POT {#pot_introduction}
+# Post-training Quantization with POT {#pot_introduction}
 
 @sphinxdirective
 
@@ -27,8 +27,8 @@ While post-training quantization makes your model run faster and take less memor
 ### Post-Training Quantization Quick Start Examples
 Try out these interactive Jupyter Notebook examples to learn the POT API and see post-training quantization in action:
 
-* [Quantization of Image Classification Models with POT](https://docs.openvino.ai/2022.2/notebooks/113-image-classification-quantization-with-output.html).
+* [Quantization of Image Classification Models with POT](https://docs.openvino.ai/latest/notebooks/113-image-classification-quantization-with-output.html).
-* [Object Detection Quantization with POT](https://docs.openvino.ai/2022.2/notebooks/111-detection-quantization-with-output.html).
+* [Object Detection Quantization with POT](https://docs.openvino.ai/latest/notebooks/111-detection-quantization-with-output.html).
 
 ## Quantizing Models with POT
 The figure below shows the post-training quantization workflow with POT. In a typical workflow, a pre-trained model is converted to OpenVINO IR format using Model Optimizer. Then, the model is quantized with a representative dataset using POT.