MCT tutorials readme - fix broken links (#1010)

* Fixed broken links in MCT tutorials readme.
sony · Mar 21, 2024 · 9350658 · 9350658
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diff --git a/FAQ.md b/FAQ.md
@@ -36,7 +36,7 @@ quantized_model = mct.keras_load_quantized_model('my_model.keras')
 
 #### PyTorch
 
-PyTorch models can be exported as onnx models. An example of loading a saved onnx model can be found [here](https://sony.github.io/model_optimization/api/api_docs/modules/exporter.html#use-exported-model-for-inference).
+PyTorch models can be exported as onnx models. An example of loading a saved onnx model can be found [here](https://github.com/sony/model_optimization/blob/main/docs/api/experimental_api_docs/modules/exporter.html#use-exported-model-for-inference).
 
 *Note:* Running inference on an ONNX model in the `onnxruntime` package has a high latency.
 Inference on the target platform (e.g. the IMX500) is not affected by this latency.

diff --git a/README.md b/README.md
@@ -38,15 +38,16 @@ For installing the nightly version or installing from source, refer to the [inst
 
 ### Quick start & tutorials 
 
-For an example of how to use MCT with TensorFlow or PyTorch on various models and tasks,
-check out the [quick-start page](tutorials/quick_start/README.md) and
-the [results CSV](tutorials/quick_start/results/model_quantization_results.csv).
-
-In addition, a set of [notebooks](tutorials/notebooks) are provided for an easy start. For example:
-* [MobileNet with Tensorflow](tutorials/notebooks/keras/ptq/example_keras_mobilenet.py).
-* [MobileNetV2 with PyTorch](tutorials/notebooks/pytorch/ptq/example_pytorch_mobilenet_v2.py).
+Explore the Model Compression Toolkit (MCT) through our tutorials, 
+covering compression techniques for Keras and PyTorch models. Access interactive [notebooks](tutorials/README.md) 
+for hands-on learning. For example:
+* [Keras MobileNetV2 post training quantization](tutorials/notebooks/keras/ptq/example_keras_imagenet.ipynb)
+* [Post training quantization with PyTorch](tutorials/notebooks/pytorch/ptq/example_pytorch_quantization_mnist.ipynb)
 * [Data Generation for ResNet18 with PyTorch](tutorials/notebooks/pytorch/data_generation/example_pytorch_data_generation.ipynb).
 
+Additionally, for quick quantization of a variety of models from well-known collections,
+visit the [quick-start page](tutorials/quick_start/README.md) and the
+[results CSV](tutorials/quick_start/results/model_quantization_results.csv).
 
 ### Supported Versions
 

diff --git a/tutorials/README.md b/tutorials/README.md
@@ -6,21 +6,21 @@ Access interactive Jupyter notebooks for hands-on learning.
 
 ## Getting started
 Learn how to quickly quantize pre-trained models using MCT's post-training quantization technique for both Keras and PyTorch models.
-- [Keras MobileNetV2 post training quantization](notebooks/keras/ptq/example_keras_imagenet.ipynb)
-- [Pytorch MobileNetV2 post training quantization](notebooks/pytorch/ptq/example_pytorch_quantization_mnist.ipynb)
+- [Post training quantization with Keras](notebooks/keras/ptq/example_keras_imagenet.ipynb)
+- [Post training quantization with PyTorch](notebooks/pytorch/ptq/example_pytorch_quantization_mnist.ipynb)
 
 ## MCT Features
 This set of tutorials covers all the quantization tools provided by MCT. 
 The notebooks in this section demonstrate how to configure and run simple and advanced post-training quantization methods.
 This includes fine-tuning PTQ (Post-Training Quantization) configurations, exporting models,
 and exploring advanced compression techniques. 
 These techniques are essential for further optimizing models and achieving superior performance in deployment scenarios.
-- [MCT notebooks](notebooks/MCT_notebooks.md)
+- [MCT Features notebooks](notebooks/MCT_notebooks.md)
 
 ## Quantization for Sony-IMX500 deployment
 
 This section provides several guides on quantizing pre-trained models to meet specific constraints for deployment on the
 [Sony-IMX500](https://developer.sony.com/imx500/) processing platform. 
 We will cover various tasks and demonstrate the necessary steps to achieve efficient quantization for optimal
 deployment performance.
-- [IMX500 notebooks](notebooks/IMX500_notebooks.md)
+- [MCT IMX500 notebooks](notebooks/IMX500_notebooks.md)
diff --git a/tutorials/notebooks/IMX500_notebooks.md b/tutorials/notebooks/IMX500_notebooks.md
@@ -6,7 +6,7 @@ deployment performance.
 
   | Task                                                            | Model          | Source Repository                                                   | Notebook                                                                                                |
   |-----------------------------------------------------------------|----------------|---------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|
-  | Classification                                                  | MobileNetV2    | [Keras Applications](https://keras.io/api/applications/)            | [Keras notebook](model_optimization/tutorials/notebooks/keras/ptq/example_keras_imagenet.ipynb)         |
-  | Object Detection                                                | YOLOv8n        | [Ultralytics](https://github.com/ultralytics/ultralytics)           | [Keras notebook](model_optimization/tutorials/notebooks/keras/ptq/keras_yolov8n_for_imx500.ipynb)       | 
-  | Semantic Segmentation                                           | DeepLabV3-Plus | [bonlime's repo](https://github.com/bonlime/keras-deeplab-v3-plus)  | [Keras notebook](model_optimization/tutorials/notebooks/keras/ptq/keras_deeplabv3plus_for_imx500.ipynb) |
+  | Classification                                                  | MobileNetV2    | [Keras Applications](https://keras.io/api/applications/)            | [Keras notebook](keras/ptq/example_keras_imagenet.ipynb)         |
+  | Object Detection                                                | YOLOv8n        | [Ultralytics](https://github.com/ultralytics/ultralytics)           | [Keras notebook](keras/ptq/keras_yolov8n_for_imx500.ipynb)       | 
+  | Semantic Segmentation                                           | DeepLabV3-Plus | [bonlime's repo](https://github.com/bonlime/keras-deeplab-v3-plus)  | [Keras notebook](keras/ptq/keras_deeplabv3plus_for_imx500.ipynb) |
 
diff --git a/tutorials/notebooks/MCT_notebooks.md b/tutorials/notebooks/MCT_notebooks.md
@@ -92,14 +92,6 @@ These techniques are essential for further optimizing models and achieving super
 
 
 
-<details id="pytorch-qat">
-  <summary>Quantization-Aware Training (QAT)</summary>
-
-  | Tutorial                                                                          | Included Features      |
-  |-----------------------------------------------------------------------------------|--------------|
-  | [QAT on MNIST](pytorch/qat/example_pytorch_qat.py) | &#x2705; QAT |
-</details>
-
 </details>
 
 <details id="pytorch-pruning">