update (#18615)

docs/notebooks/115-async-api-with-output.rst

@@ -91,7 +91,7 @@ Load the model
     # read the network and corresponding weights from file
     model = ie.read_model(model=model_path)
     
-    # compile the model for the CPU (you can choose manually CPU, GPU, MYRIAD etc.)
+    # compile the model for the CPU (you can choose manually CPU, GPU, etc.)
     # or let the engine choose the best available device (AUTO)
     compiled_model = ie.compile_model(model=model, device_name="CPU")
     

docs/notebooks/211-speech-to-text-with-output.rst

@@ -393,7 +393,7 @@ Now, you can read and load the network.
     ie = Core()
 
 You may run the network on multiple devices. By default, it will load
-the model on CPU (you can choose manually CPU, GPU, MYRIAD, etc.) or let
+the model on CPU (you can choose manually CPU, GPU, etc.) or let
 the engine choose the best available device (AUTO).
 
 To list all available devices that can be used, run

docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst

@@ -144,7 +144,7 @@ specified device.
     
         # Read the network and corresponding weights from a file.
         model = ie_core.read_model(model=model_path)
-        # Compile the model for CPU (you can use GPU or MYRIAD as well).
+        # Compile the model for CPU (you can also use GPU).
         compiled_model = ie_core.compile_model(model=model, device_name="CPU")
         # Get input and output names of nodes.
         input_keys = compiled_model.input(0)

docs/notebooks/301-tensorflow-training-openvino-with-output.rst

@@ -900,9 +900,8 @@ OpenVINO Inference Engine Setup
     ie = Core()
     
     # Neural Compute Stick
-    # compile the model for the CPU (you can choose manually CPU, GPU, MYRIAD etc.)
+    # compile the model for the CPU (you can choose manually CPU, GPU, etc.)
     # or let the engine choose the best available device (AUTO)
-    # compiled_model = ie.compile_model(model=model, device_name="MYRIAD")
     compiled_model = ie.compile_model(model=ir_model, device_name="CPU")
     
     del ir_model

docs/notebooks/402-pose-estimation-with-output.rst

@@ -97,7 +97,7 @@ desired device.
     ie_core = Core()
     # Read the network from a file.
     model = ie_core.read_model(model_path)
-    # Let the AUTO device decide where to load the model (you can use CPU, GPU or MYRIAD as well).
+    # Let the AUTO device decide where to load the model (you can use CPU or GPU).
     compiled_model = ie_core.compile_model(model=model, device_name="AUTO", config={"PERFORMANCE_HINT": "LATENCY"})
     
     # Get the input and output names of nodes.

docs/notebooks/403-action-recognition-webcam-with-output.rst

@@ -183,7 +183,7 @@ Model Initialization function
     
         # Read the network and corresponding weights from a file.
         model = ie_core.read_model(model=model_path)
-        # Compile the model for CPU (you can use GPU or MYRIAD as well).
+        # Compile the model for CPU (you can also use GPU).
         compiled_model = ie_core.compile_model(model=model, device_name="CPU")
         # Get input and output names of nodes.
         input_keys = compiled_model.input(0)

docs/notebooks/404-style-transfer-with-output.rst

@@ -164,7 +164,7 @@ results.
     # Read the network and corresponding weights from IR Model.
     model = ie_core.read_model(model=ir_path)
     
-    # Compile the model for CPU (or change to GPU, MYRIAD etc. for other devices)
+    # Compile the model for CPU (or change to GPU, etc. for other devices)
     # or let OpenVINO select the best available device with AUTO.
     compiled_model = ie_core.compile_model(model=model, device_name="AUTO")
     

docs/notebooks/406-3D-pose-estimation-with-output.rst

@@ -209,7 +209,7 @@ created to infer the compiled model.
     ie_core = Core()
     # read the network and corresponding weights from file
     model = ie_core.read_model(model=ir_model_path, weights=model_weights_path)
-    # load the model on the CPU (you can use GPU or MYRIAD as well)
+    # load the model on the CPU (you can also use GPU)
     compiled_model = ie_core.compile_model(model=model, device_name="CPU")
     infer_request = compiled_model.create_infer_request()
     input_tensor_name = model.inputs[0].get_any_name()

docs/tutorials.md

@@ -117,8 +117,6 @@ Tutorials that explain how to optimize and quantize models with OpenVINO tools.
    +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
    | `107-speech-recognition-quantization <notebooks/107-speech-recognition-quantization-data2vec-with-output.html>`__ |br| |c107|                      | Optimize and quantize a pre-trained Data2Vec speech model.                                                                       |
    +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
-   | `108-gpu-device <notebooks/108-gpu-device-with-output.html>`__                                                                                     | Working with GPUs in OpenVINO™.                                                                                                  |
-   +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
    | `109-performance-tricks <notebooks/109-latency-tricks-with-output.html>`__                                                                         | Performance tricks in OpenVINO™.                                                                                                 |
    +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
    | `110-ct-segmentation-quantize <notebooks/110-ct-scan-live-inference-with-output.html>`__ |br| |n110|                                               | Live inference of a kidney segmentation model and benchmark CT-scan data with OpenVINO.                                          |