diff --git a/source/guides/maintenance/developer-workstation.rst b/source/guides/maintenance/developer-workstation.rst
index c3e0bb97..5333fedc 100644
--- a/source/guides/maintenance/developer-workstation.rst
+++ b/source/guides/maintenance/developer-workstation.rst
@@ -78,9 +78,6 @@ tools you need to start. Consider these profiles as a starting point.
* - Work with deep learning and edge-optimized models.
- `computer-vision-models `_
- * - Basic OpenVINO™ toolkit.
- - `computer-vision-openvino `_
-
* - API helper for cloud access.
- `cloud-api `_
diff --git a/source/tutorials/index.rst b/source/tutorials/index.rst
index ec35151c..6773db3d 100644
--- a/source/tutorials/index.rst
+++ b/source/tutorials/index.rst
@@ -37,7 +37,6 @@ sorted by difficulty level! Learn about :ref:`how we evaluate tutorials -o
-
- .. note::
-
- * Where :file:`` is the one you chose from previous step
-
- * Where :file:`` is your project directory
-
-#. For this example, enter:
-
- .. code-block:: bash
-
- python3 downloader.py --name squeezenet1.1 -o $HOME/.
-
-#. After running this command, the model appears as downloading at your
- :file:`$HOME/classification/squeezenet/1.1/caffe` as follows:
-
- .. code-block:: console
-
- ###############|| Downloading topologies ||###############
-
- ========= Downloading /$HOME/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel
- ... 100%, 4834 KB, 2839 KB/s, 1 seconds passed
-
- ...
-
-Convert model to IR format
-==========================
-
-#. As necessary, follow the instruction on :ref:`convert-dl-models`
- to convert deep learning models.
-
-#. Navigate to the model:
-
- .. code-block:: bash
-
- cd $HOME/classification/squeezenet/1.1/caffe
-
-#. Enter the command:
-
- .. code-block:: bash
-
- python3 /usr/share/openvino/model-optimizer/mo.py --input_model squeezenet1.1.caffemodel
-
-
- The output will show these files being generated:
-
- .. code-block:: console
-
- squeezenet1.1.xml
-
- squeezenet1.1.bin
-
-#. Finally, enter :command:`ls` to view the newly added model and files.
-
-Run image classification
-========================
-
-This sample application demonstrates how to run the Image Classification in asynchronous mode on supported devices. In this example, we use the image of a specific type of automobile to test the inference engine. Squeezenet 1.1 is designed to perform image classification and has been trained on the `ImageNet`_ database.
-
-#. We provide an image of an automobile, shown in Figure 1. For ease of use,
- save this image into the :file:`classification` model directory.
-
- .. figure:: ../_figures/openvino/automobile.png
- :height: 375 px
- :width: 500 px
- :scale: 100 %
- :alt: Photo by Goh Rhy Yan on Unsplash
-
- Figure 1: Photo by Goh Rhy Yan on Unsplash
-
-#. To execute the sample application enter the command:
-
- .. code-block:: bash
-
- classification_sample_async -i -m -d
-
- .. note::
-
- * Where :file:`` is the image that you selected
-
- * Where :file:`` is the path to the IR model file
-
- * Where :file:`` is your choice of CPU, GPU, etc.
-
-#. In this case, we replace the :file:`` with the previously
- saved image for CPU inferencing.
-
- .. code-block:: bash
-
- classification_sample_async -i ./automobile.png -m squeezenet1.1.xml
-
- .. note::
-
- If you do not specify the :file:`device`, the CPU is used by default.
-
-#. The results show the highest probability is 67% for a sports car.
-
- .. code-block:: bash
-
- classid probability
- ------- -----------
- 817 0.6717085
- 511 0.1611409
-
- +-----------------------+-----------------------------------+
- |:command:`classid` 817 | :command:`sports car, sport car` |
- +-----------------------+-----------------------------------+
- |:command:`classid` 511 |:command:`convertible` |
- +-----------------------+-----------------------------------+
-
- .. note:
-
- Label definitions are provided by `ImageNet`_.
-
-#. Next, add :command:`-d GPU` to the end of the above command for GPU
- inferencing.
-
- .. code-block:: bash
-
- classification_sample_async -i ./automobile.png -m squeezenet1.1.xml -d GPU
-
-Run benchmark_app
-*****************
-
-This sample application demonstrates how to use benchmark application to
-estimate deep learning inference **performance** on supported devices.
-We use the same image of an automobile, Figure 1, from the previous section.
-
-#. To execute this sample application, enter:
-
- .. code-block:: bash
-
- benchmark_app -i -m -d
-
- .. note::
-
- * Where :file:`` is the image that you selected
-
- * Where :file:`` is the path to the IR model file
-
- * Where :file:`` is local your choice of CPU, GPU, etc.
-
-#. Change directory:
-
- .. code-block:: bash
-
- cd $HOME/classification/squeezenet/1.1/caffe
-
-#. Enter the following command for CPU inferencing.
-
- .. code-block:: bash
-
- benchmark_app -i ./automobile.png -m squeezenet1.1.xml
-
-#. For the CPU, the results show a :guilabel:`Throughput` of 243.202 FPS.
-
- .. code-block:: console
- :linenos:
- :emphasize-lines: 4
-
- Count: 1464 iterations
- Duration: 60196.8 ms
- Latency: 164.104 ms
- Throughput: 243.202 FPS
-
-#. Next, add :command:`-d GPU` to the end of the same command for GPU
- inferencing.
-
- .. code-block:: bash
-
- benchmark_app -i ./automobile.png -m squeezenet1.1.xml -d GPU
-
-#. For the GPU, the results show a :guilabel:`Throughput` of 372.677 FPS.
-
- .. code-block:: console
- :linenos:
- :emphasize-lines: 4
-
- Count: 2240 iterations
- Duration: 60105.7 ms
- Latency: 107.554 ms
- Throughput: 372.677 FPS
-
-.. _ImageNet: http://image-net.org/