fix outdated links on documentation (#1669)

Author: eaidova

README.md

@@ -396,7 +396,7 @@ Made with [`contrib.rocks`](https://contrib.rocks).
 
 ## ❓ FAQ
 
-* [Which devices does OpenVINO support?](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html#doxid-openvino-docs-o-v-u-g-supported-plugins-supported-devices)
+* [Which devices does OpenVINO support?](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html#doxid-openvino-docs-o-v-u-g-supported-plugins-supported-devices)
 * [What is the first CPU generation you support with OpenVINO?](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
 * [Are there any success stories about deploying real-world solutions with OpenVINO?](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html)
 

README_cn.md

@@ -367,7 +367,7 @@ jupyter lab notebooks
 
 ## ❓ 常见问题解答
 
-* [OpenVINO支持哪些设备？](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html#doxid-openvino-docs-o-v-u-g-supported-plugins-supported-devices)
+* [OpenVINO支持哪些设备？](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html#doxid-openvino-docs-o-v-u-g-supported-plugins-supported-devices)
 * [OpenVINO支持的第一代CPU是什么？](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
 * [在使用OpenVINO部署现实世界解决方案方面有没有成功的案例？](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html)
 

notebooks/001-hello-world/001-hello-world.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This basic introduction to OpenVINO™ shows how to do inference with an image classification model.\n",
     "\n",
-    "A pre-trained [MobileNetV3 model](https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the [TensorFlow to OpenVINO](../101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb) tutorial.\n",
+    "A pre-trained [MobileNetV3 model](https://docs.openvino.ai/2023.3/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the [TensorFlow to OpenVINO](../101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb) tutorial.\n",
     "\n",
     "#### Table of contents:\n",
     "- [Imports](#Imports)\n",

notebooks/003-hello-segmentation/003-hello-segmentation.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "A very basic introduction to using segmentation models with OpenVINO™.\n",
     "\n",
-    "In this tutorial, a pre-trained [road-segmentation-adas-0001](https://docs.openvino.ai/2023.0/omz_models_model_road_segmentation_adas_0001.html) model from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.\n",
+    "In this tutorial, a pre-trained [road-segmentation-adas-0001](https://docs.openvino.ai/2023.3/omz_models_model_road_segmentation_adas_0001.html) model from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.\n",
     "\n",
     "#### Table of contents:\n",
     "- [Imports](#Imports)\n",

notebooks/003-hello-segmentation/README.md

@@ -11,7 +11,7 @@ This notebook demonstrates how to do inference with segmentation model.
 
 ## Notebook Contents
 
-A very basic introduction to segmentation with OpenVINO. This notebook uses the [`road-segmentation-adas-0001`](https://docs.openvino.ai/2023.0/omz_models_model_road_segmentation_adas_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) and an input image downloaded from [Mapillary Vistas](https://www.mapillary.com/dataset/vistas). ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.
+A very basic introduction to segmentation with OpenVINO. This notebook uses the [`road-segmentation-adas-0001`](https://docs.openvino.ai/2023.3/omz_models_model_road_segmentation_adas_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) and an input image downloaded from [Mapillary Vistas](https://www.mapillary.com/dataset/vistas). ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.
 
 ## Installation Instructions
 

notebooks/004-hello-detection/004-hello-detection.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "A very basic introduction to using object detection models with OpenVINO™.\n",
     "\n",
-    "The [horizontal-text-detection-0001](https://docs.openvino.ai/2023.0/omz_models_model_horizontal_text_detection_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects horizontal text in images and returns a blob of data in the shape of `[100, 5]`. Each detected text box is stored in the `[x_min, y_min, x_max, y_max, conf]` format, where the\n",
+    "The [horizontal-text-detection-0001](https://docs.openvino.ai/2023.3/omz_models_model_horizontal_text_detection_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects horizontal text in images and returns a blob of data in the shape of `[100, 5]`. Each detected text box is stored in the `[x_min, y_min, x_max, y_max, conf]` format, where the\n",
     "`(x_min, y_min)` are the coordinates of the top left bounding box corner, `(x_max, y_max)` are the coordinates of the bottom right bounding box corner and `conf` is the confidence for the predicted class.\n",
     "\n",
     "#### Table of contents:\n",

notebooks/004-hello-detection/README.md

@@ -12,7 +12,7 @@ This notebook demonstrates how to do inference with detection model.
 
 ## Notebook Contents
 
-In this basic introduction to detection with OpenVINO, the [horizontal-text-detection-0001](https://docs.openvino.ai/2023.0/omz_models_model_horizontal_text_detection_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects text in images and returns blob of data in shape of `[100, 5]`. For each detection, a description is in the `[x_min, y_min, x_max, y_max, conf]` format.
+In this basic introduction to detection with OpenVINO, the [horizontal-text-detection-0001](https://docs.openvino.ai/2023.3/omz_models_model_horizontal_text_detection_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects text in images and returns blob of data in shape of `[100, 5]`. For each detection, a description is in the `[x_min, y_min, x_max, y_max, conf]` format.
 
 ## Installation Instructions
 

notebooks/101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Convert a TensorFlow Model to OpenVINO™\n",
     "\n",
-    "This short tutorial shows how to convert a TensorFlow [MobileNetV3](https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) image classification model to OpenVINO [Intermediate Representation](https://docs.openvino.ai/2023.3/openvino_docs_MO_DG_IR_and_opsets.html) (OpenVINO IR) format, using [Model Conversion API](https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html). After creating the OpenVINO IR, load the model in [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and do inference with a sample image.  \n",
+    "This short tutorial shows how to convert a TensorFlow [MobileNetV3](https://docs.openvino.ai/2023.3/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) image classification model to OpenVINO [Intermediate Representation](https://docs.openvino.ai/2023.3/openvino_docs_MO_DG_IR_and_opsets.html) (OpenVINO IR) format, using [Model Conversion API](https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html). After creating the OpenVINO IR, load the model in [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and do inference with a sample image.  \n",
     "\n",
     "#### Table of contents:\n",
     "- [Imports](#Imports)\n",

notebooks/103-paddle-to-openvino/README.md

@@ -9,7 +9,7 @@ This notebook shows how to convert [PaddlePaddle](https://www.paddlepaddle.org.c
 
 ## Notebook Contents
 
-The notebook uses [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html) to convert a MobileNet V3 [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) model, pre-trained on the [ImageNet](https://www.image-net.org) dataset, to OpenVINO IR. It also shows how to perform classification inference on an image, using [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and compares the results of the PaddlePaddle model with the OpenVINO IR model. 
+The notebook uses [model conversion API](https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html) to convert a MobileNet V3 [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) model, pre-trained on the [ImageNet](https://www.image-net.org) dataset, to OpenVINO IR. It also shows how to perform classification inference on an image, using [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and compares the results of the PaddlePaddle model with the OpenVINO IR model. 
 
 ## Installation Instructions
 

notebooks/108-gpu-device/README.md

@@ -1,6 +1,6 @@
 # Working with GPUs in OpenVINO™
 
-This notebook shows how to do inference with Graphic Processing Units (GPUs). To learn more about GPUs in OpenVINO, refer to the [GPU Device](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_GPU.html) section in the docs.
+This notebook shows how to do inference with Graphic Processing Units (GPUs). To learn more about GPUs in OpenVINO, refer to the [GPU Device](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_supported_plugins_GPU.html) section in the docs.
 
 ## Notebook Contents
 

notebooks/110-ct-segmentation-quantize/README.md

@@ -25,7 +25,7 @@ so it is not required to run the data preparation and training notebooks before
 
 This quantization tutorial consists of the following steps:
 
-* Use model conversion Python API to convert the model to OpenVINO IR. For more information about model conversion Python API, see this [page](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html).
+* Use model conversion Python API to convert the model to OpenVINO IR. For more information about model conversion Python API, see this [page](https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html).
 * Quantizing the model with NNCF with the [Post-training Quantization with NNCF Tool](https://docs.openvino.ai/nightly/basic_quantization_flow.html) API in OpenVINO.
 * Evaluating the F1 score metric of the original model and the quantized model.
 * Benchmarking performance of the original model and the quantized model.

notebooks/113-image-classification-quantization/README.md

@@ -4,7 +4,7 @@
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/113-image-classification-quantization/113-image-classification-quantization.ipynb)
 
 This tutorial demonstrates how to apply `INT8` quantization to the MobileNet V2 Image Classification model, using the 
-[NNCF Post-Training Quantization API](https://docs.openvino.ai/2023.0/ptq_introduction.html). The tutorial uses [MobileNetV2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) and [Cifar10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).
+[NNCF Post-Training Quantization API](https://docs.openvino.ai/2023.3/ptq_introduction.html). The tutorial uses [MobileNetV2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) and [Cifar10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).
 The code of the tutorial is designed to be extendable to custom models and datasets. 
 
 ## Notebook Contents

notebooks/115-async-api/README.md

@@ -4,7 +4,7 @@
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/115-async-api/115-async-api.ipynb)
 
 
-This notebook demonstrates how to use the [Async API](https://docs.openvino.ai/nightly/openvino_docs_deployment_optimization_guide_common.html) and [`AsyncInferQueue`](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Python_API_exclusives.html#asyncinferqueue) for asynchronous execution with OpenVINO.
+This notebook demonstrates how to use the [Async API](https://docs.openvino.ai/nightly/openvino_docs_deployment_optimization_guide_common.html) and [`AsyncInferQueue`](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_Python_API_exclusives.html#asyncinferqueue) for asynchronous execution with OpenVINO.
 
 OpenVINO Runtime supports inference in either synchronous or asynchronous mode. The key advantage of the Async API is that when a device is busy with inference, the application can perform other tasks in parallel (for example, populating inputs or scheduling other requests) rather than wait for the current inference to complete first.
 

notebooks/117-model-server/117-model-server.ipynb

@@ -321,7 +321,7 @@
    "id": "e8ab7f4c",
    "metadata": {},
    "source": [
-    "The required Model Server parameters are listed below. For additional configuration options, see the [Model Server Parameters section](https://docs.openvino.ai/2023.2/ovms_docs_parameters.html).\n",
+    "The required Model Server parameters are listed below. For additional configuration options, see the [Model Server Parameters section](https://docs.openvino.ai/2023.3/ovms_docs_parameters.html).\n",
     "\n",
     "<table class=\"table\">\n",
     "<colgroup>\n",
@@ -749,7 +749,7 @@
     "## References\n",
     "[back to top ⬆️](#Table-of-contents:)\n",
     "\n",
-    "1. [OpenVINO™ Model Server documentation](https://docs.openvino.ai/2023.0/ovms_what_is_openvino_model_server.html)\n",
+    "1. [OpenVINO™ Model Server documentation](https://docs.openvino.ai/2023.3/ovms_what_is_openvino_model_server.html)\n",
     "2. [OpenVINO™ Model Server GitHub repository](https://github.com/openvinotoolkit/model_server/)"
    ]
   }

notebooks/118-optimize-preprocessing/README.md

@@ -2,7 +2,7 @@
 
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/118-optimize-preprocessing/118-optimize-preprocessing.ipynb)
 
-This tutorial demonstrates how the image could be transform to the data format expected by the model with Preprocessing API. Preprocessing API is an easy-to-use instrument, that enables integration of preprocessing steps into an execution graph and perform it on selected device, which can improve of device utilization. For more information about Preprocessing API, please, see this [overview](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Preprocessing_Overview.html#) and [details](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Preprocessing_Details.html). The tutorial uses [InceptionResNetV2](https://www.tensorflow.org/api_docs/python/tf/keras/applications/inception_resnet_v2) model.
+This tutorial demonstrates how the image could be transform to the data format expected by the model with Preprocessing API. Preprocessing API is an easy-to-use instrument, that enables integration of preprocessing steps into an execution graph and perform it on selected device, which can improve of device utilization. For more information about Preprocessing API, please, see this [overview](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_Preprocessing_Overview.html#) and [details](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_Preprocessing_Details.html). The tutorial uses [InceptionResNetV2](https://www.tensorflow.org/api_docs/python/tf/keras/applications/inception_resnet_v2) model.
 
 
 ## Notebook Contents

notebooks/119-tflite-to-openvino/README.md

@@ -7,7 +7,7 @@ This tutorial explains how to convert [TensorFlow Lite](https://www.tensorflow.o
 
 ## Notebook Contents
 
-The notebook uses [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html) to convert model to OpenVINO Intermediate Representation format.
+The notebook uses [model conversion API](https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html) to convert model to OpenVINO Intermediate Representation format.
 
 ## Installation Instructions
 

notebooks/122-quantizing-model-with-accuracy-control/README.md

@@ -6,7 +6,7 @@ These tutorials demonstrate how to apply 8-bit quantization with accuracy contro
 
 The code of the tutorials is designed to be extendable to the same model types trained on custom datasets.
 
-The advanced quantization flow allows to apply 8-bit quantization to the model with control of accuracy metric. This is achieved by keeping the most impactful operations within the model in the original precision. The flow is based on the [Quantizing with Accuracy Control](https://docs.openvino.ai/2023.0/quantization_w_accuracy_control.html) and has the following specifics:
+The advanced quantization flow allows to apply 8-bit quantization to the model with control of accuracy metric. This is achieved by keeping the most impactful operations within the model in the original precision. The flow is based on the [Quantizing with Accuracy Control](https://docs.openvino.ai/2023.3/quantization_w_accuracy_control.html) and has the following specifics:
 
 - Besides the calibration dataset, a validation dataset is required to compute the accuracy metric. Both datasets can refer to the same data in the simplest case.
 - Validation function, used to compute accuracy metric is required. It can be a function that is already available in the source framework or a custom function.

notebooks/201-vision-monodepth/201-vision-monodepth.ipynb

@@ -12,7 +12,7 @@
    "source": [
     "# Monodepth Estimation with OpenVINO\n",
     "\n",
-    "This tutorial demonstrates Monocular Depth Estimation with MidasNet in OpenVINO. Model information can be found [here](https://docs.openvino.ai/2023.0/omz_models_model_midasnet.html).\n",
+    "This tutorial demonstrates Monocular Depth Estimation with MidasNet in OpenVINO. Model information can be found [here](https://docs.openvino.ai/2023.3/omz_models_model_midasnet.html).\n",
     "\n",
     "![monodepth](https://user-images.githubusercontent.com/36741649/127173017-a0bbcf75-db24-4d2c-81b9-616e04ab7cd9.gif)\n",
     "\n",

notebooks/202-vision-superresolution/202-vision-superresolution-image.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Single Image Super Resolution with OpenVINO™\n",
     "\n",
-    "Super Resolution is the process of enhancing the quality of an image by increasing the pixel count using deep learning. This notebook shows the Single Image Super Resolution (SISR) which takes just one low resolution image. A model called [single-image-super-resolution-1032](https://docs.openvino.ai/2023.0/omz_models_model_single_image_super_resolution_1032.html), which is available in Open Model Zoo, is used in this tutorial. It is based on the research paper cited below.\n",
+    "Super Resolution is the process of enhancing the quality of an image by increasing the pixel count using deep learning. This notebook shows the Single Image Super Resolution (SISR) which takes just one low resolution image. A model called [single-image-super-resolution-1032](https://docs.openvino.ai/2023.3/omz_models_model_single_image_super_resolution_1032.html), which is available in Open Model Zoo, is used in this tutorial. It is based on the research paper cited below.\n",
     "\n",
     "Y. Liu et al., [\"An Attention-Based Approach for Single Image Super Resolution,\"](https://arxiv.org/abs/1807.06779) 2018 24th International Conference on Pattern Recognition (ICPR), 2018, pp. 2777-2784, doi: 10.1109/ICPR.2018.8545760.\n",
     "\n",