[DOCS] New notebooks update for master

docs/articles_en/about-openvino/release-notes-openvino.rst

@@ -232,18 +232,18 @@ Jupyter Notebooks
 
 The following notebooks have been updated or newly added:
 
-* `Mobile language assistant with MobileVLM <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/279-mobilevlm-language-assistant>`__
-* `Depth estimation with DepthAnything <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/280-depth-anything>`__
-* `Kosmos-2 <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/281-kosmos2-multimodal-large-language-model>`__
-* `Zero-shot Image Classification with SigLIP <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/282-siglip-zero-shot-image-classification>`__
-* `Personalized image generation with PhotoMaker <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/283-photo-maker>`__
-* `Voice tone cloning with OpenVoice <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/284-openvoice>`__
-* `Line-level text detection with Surya <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/285-surya-line-level-text-detection>`__
-* `InstantID: Zero-shot Identity-Preserving Generation using OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/286-instant-id>`__
-* `Tutorial for Big Image Transfer  (BIT) model quantization using NNCF <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/127-big-transfer-quantization>`__
-* `Tutorial for OpenVINO Tokenizers integration into inference pipelines <https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/128-openvino-tokenizers>`__
-* `LLM chatbot <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/254-llm-chatbot/254-llm-chatbot.ipynb>`__ and
-  `LLM RAG pipeline <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/254-llm-chatbot/254-rag-chatbot.ipynb>`__
+* `Mobile language assistant with MobileVLM <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/mobilevlm-language-assistant>`__
+* `Depth estimation with DepthAnything <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/depth-anything>`__
+* `Kosmos-2 <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/kosmos2-multimodal-large-language-model>`__
+* `Zero-shot Image Classification with SigLIP <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/siglip-zero-shot-image-classification>`__
+* `Personalized image generation with PhotoMaker <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/photo-maker>`__
+* `Voice tone cloning with OpenVoice <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/openvoice>`__
+* `Line-level text detection with Surya <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/surya-line-level-text-detection>`__
+* `InstantID: Zero-shot Identity-Preserving Generation using OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/instant-id>`__
+* `Tutorial for Big Image Transfer  (BIT) model quantization using NNCF <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/big-transfer-quantization>`__
+* `Tutorial for OpenVINO Tokenizers integration into inference pipelines <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/openvino-tokenizers>`__
+* `LLM chatbot <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot.ipynb>`__ and
+  `LLM RAG pipeline <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/rag-chatbot.ipynb>`__
   have received integration with new models: minicpm-2b-dpo, gemma-7b-it, qwen1.5-7b-chat, baichuan2-7b-chat
 
 

docs/articles_en/documentation/legacy-features/install-dev-tools.rst

@@ -176,13 +176,13 @@ Get started with Python
 .. image:: ../../_static/images/get_started_with_python.gif
   :width: 400
 
-Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
+Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
 
 Visit the :doc:`Tutorials <../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
-* `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__
-* `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__
-* `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__
+* `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
+* `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
+* `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__
 
 Get started with C++
 ++++++++++++++++++++

docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats.rst

@@ -76,7 +76,7 @@ Here are code examples of how to use these methods with different model formats:
 
               For more details on conversion, refer to the
               :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-pytorch>`
-              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/pytorch-onnx-to-openvino-with-output.html>`__
               on this topic.
 
    .. tab-item:: TensorFlow
@@ -115,14 +115,14 @@ Here are code examples of how to use these methods with different model formats:
 
                  import openvino
                  from openvino.tools.mo import convert_model
-                 
+
                  core = openvino.Core()
                  ov_model = convert_model("saved_model.pb")
                  compiled_model = core.compile_model(ov_model, "AUTO")
 
               For more details on conversion, refer to the
               :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-tensorflow>`
-              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/101-tensorflow-to-openvino-with-output.html>`__
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/tensorflow-to-openvino-with-output.html>`__
               on this topic.
 
             * The ``read_model()`` and ``compile_model()`` methods:
@@ -227,7 +227,7 @@ Here are code examples of how to use these methods with different model formats:
 
               For more details on conversion, refer to the
               :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-tensorflow>`
-              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/119-tflite-to-openvino-with-output.html>`__
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/tflite-to-openvino-with-output.html>`__
               on this topic.
 
 
@@ -260,7 +260,7 @@ Here are code examples of how to use these methods with different model formats:
                  :force:
 
                  import openvino
-                 
+
                  core = openvino.Core()
                  compiled_model = core.compile_model("<INPUT_MODEL>.tflite", "AUTO")
 
@@ -355,7 +355,7 @@ Here are code examples of how to use these methods with different model formats:
 
               For more details on conversion, refer to the
               :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-onnx>`
-              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/pytorch-onnx-to-openvino-with-output.html>`__
               on this topic.
 
 
@@ -486,7 +486,7 @@ Here are code examples of how to use these methods with different model formats:
 
               For more details on conversion, refer to the
               :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-paddle>`
-              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/103-paddle-to-openvino-classification-with-output.html>`__
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/paddle-to-openvino-classification-with-output.html>`__
               on this topic.
 
             * The ``read_model()`` method:

docs/articles_en/get-started.rst

@@ -24,7 +24,7 @@ GET STARTED
    <link rel="stylesheet" type="text/css" href="_static/css/getstarted_style.css">
 
    <p id="GSG_introtext">Welcome to OpenVINO! This guide introduces installation and learning materials for Intel® Distribution of OpenVINO™ toolkit. The guide walks through the following steps:<br />
-     <a href="notebooks/201-vision-monodepth-with-output.html" >Quick Start Example</a>
+     <a href="notebooks/vision-monodepth-with-output.html" >Quick Start Example</a>
      <a href="get-started/install-openvino.html" >Install OpenVINO</a>
      <a href="#learn-openvino" >Learn OpenVINO</a>
    </p>
@@ -43,7 +43,7 @@ For a quick reference, check out
 .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
    :width: 400
 
-Try out OpenVINO's capabilities with this `quick start example <notebooks/201-vision-monodepth-with-output.html>`__ that estimates depth in a scene using an OpenVINO monodepth model to quickly see how to load a model, prepare an image, inference the image, and display the result.
+Try out OpenVINO's capabilities with this `quick start example <notebooks/vision-monodepth-with-output.html>`__ that estimates depth in a scene using an OpenVINO monodepth model to quickly see how to load a model, prepare an image, inference the image, and display the result.
 
 .. _install-openvino-gsg:
 
@@ -75,10 +75,10 @@ Interactive Tutorials - Jupyter Notebooks
 
 Start with :doc:`interactive Python <learn-openvino/interactive-tutorials-python>` that show the basics of model inferencing, the OpenVINO API, how to convert models to OpenVINO format, and more.
 
-* `Hello Image Classification <notebooks/001-hello-world-with-output.html>`__ - Load an image classification model in OpenVINO and use it to apply a label to an image
-* `OpenVINO Runtime API Tutorial <notebooks/002-openvino-api-with-output.html>`__ - Learn the basic Python API for working with models in OpenVINO
-* `Convert TensorFlow Models to OpenVINO <notebooks/101-tensorflow-classification-to-openvino-with-output.html>`__
-* `Convert PyTorch Models to OpenVINO <notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
+* `Hello Image Classification <notebooks/hello-world-with-output.html>`__ - Load an image classification model in OpenVINO and use it to apply a label to an image
+* `OpenVINO Runtime API Tutorial <notebooks/openvino-api-with-output.html>`__ - Learn the basic Python API for working with models in OpenVINO
+* `Convert TensorFlow Models to OpenVINO <notebooks/tensorflow-classification-to-openvino-with-output.html>`__
+* `Convert PyTorch Models to OpenVINO <notebooks/pytorch-onnx-to-openvino-with-output.html>`__
 
 .. _code-samples:
 
@@ -106,8 +106,8 @@ Model Compression and Quantization
 
 Use OpenVINO’s model compression tools to reduce your model’s latency and memory footprint while maintaining good accuracy.
 
-* Tutorial - `Quantization-Aware Training in TensorFlow with OpenVINO NNCF <notebooks/305-tensorflow-quantization-aware-training-with-output>`__
-* Tutorial - `Quantization-Aware Training in PyTorch with NNCF <notebooks/302-pytorch-quantization-aware-training-with-output>`__
+* Tutorial - `Quantization-Aware Training in TensorFlow with OpenVINO NNCF <notebooks/tensorflow-quantization-aware-training-with-output>`__
+* Tutorial - `Quantization-Aware Training in PyTorch with NNCF <notebooks/pytorch-quantization-aware-training-with-output>`__
 * :doc:`Model Optimization Guide <openvino-workflow/model-optimization>`
 
 Automated Device Configuration

docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst

@@ -283,17 +283,17 @@ Learn more about how to integrate a model in OpenVINO applications by trying out
    .. tab-item:: Get started with Python
       :sync: get-started-py
 
-      Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__
+      Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__
       to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
 
       .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
          :width: 400
 
       Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
-      * `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__
-      * `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__
-      * `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__
+      * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
+      * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
+      * `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__
 
 
    .. tab-item:: Get started with C++

docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst

@@ -156,16 +156,16 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine
    .. tab-item:: Get started with Python
       :sync: get-started-py
 
-      Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
+      Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
 
       .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
          :width: 400
 
       Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
-      * `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__
-      * `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__
-      * `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__
+      * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
+      * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
+      * `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__
 
    .. tab-item:: Get started with C++
       :sync: get-started-cpp

docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst

@@ -194,16 +194,16 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine
    .. tab-item:: Get started with Python
       :sync: get-started-py
 
-      Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
+      Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
 
       .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
          :width: 400
 
       Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
-      * `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__
-      * `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__
-      * `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__
+      * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
+      * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
+      * `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__
 
    .. tab-item:: Get started with C++
       :sync: get-started-cpp

docs/articles_en/get-started/install-openvino/install-openvino-pip.rst

@@ -135,16 +135,16 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine
 .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
    :width: 400
 
-Try the `Python Quick Start Example <https://docs.openvino.ai/2024/notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
+Try the `Python Quick Start Example <https://docs.openvino.ai/2024/notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
 
 Get started with Python
 +++++++++++++++++++++++
 
 Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
-* `OpenVINO Python API Tutorial <https://docs.openvino.ai/2024/notebooks/002-openvino-api-with-output.html>`__
-* `Basic image classification program with Hello Image Classification <https://docs.openvino.ai/2024/notebooks/001-hello-world-with-output.html>`__
-* `Convert a PyTorch model and use it for image background removal <https://docs.openvino.ai/2024/notebooks/205-vision-background-removal-with-output.html>`__
+* `OpenVINO Python API Tutorial <https://docs.openvino.ai/2024/notebooks/openvino-api-with-output.html>`__
+* `Basic image classification program with Hello Image Classification <https://docs.openvino.ai/2024/notebooks/hello-world-with-output.html>`__
+* `Convert a PyTorch model and use it for image background removal <https://docs.openvino.ai/2024/notebooks/vision-background-removal-with-output.html>`__
 
 
 

docs/articles_en/learn-openvino/interactive-tutorials-python.rst

@@ -43,7 +43,7 @@ on how to run and manage the notebooks on your system.
 Additional Resources
 ######################
 
-* `OpenVINO™ Notebooks - Github Repository <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md>`_
+* `OpenVINO™ Notebooks - Github Repository <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/README.md>`_
 * `Binder documentation <https://mybinder.readthedocs.io/en/latest/>`_
 * `Google Colab <https://colab.research.google.com/>`__