Update README.md

Author: jimmytwei

AI-and-Analytics/Features-and-Functionality/README.md

@@ -19,15 +19,15 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 |Deep Learning| Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Quantization Aware Training](INC_QuantizationAwareTraining_TextClassification)                     | Fine-tune a BERT tiny model for emotion classification task using Quantization Aware Training and Inference from Intel® Neural Compressor (INC).
 |Deep Learning| Intel® Extension for PyTorch (IPEX) | [IntelPyTorch Extensions Inference Optimization](IntelPyTorch_Extensions_Inference_Optimization)   | Apply Intel® Extension for PyTorch (IPEX) to a PyTorch workload to gain performance boost.
 |Deep Learning| Intel® Extension for PyTorch (IPEX) | [IntelPyTorch Extensions GPU Inference Optimization with AMP](IntelPyTorch_GPU_InferenceOptimization_with_AMP)   | Use the PyTorch ResNet50 model transfer learning and inference using the CIFAR10 dataset on Intel discrete GPU with Intel® Extension for PyTorch (IPEX).
-|Deep Learning| Intel® Extension for PyTorch (IPEX)| [IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8](IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8)   | Analyze inference performance improvements using Intel® Extension for PyTorch (IPEX) with Advanced Matrix Extensions (Intel® AMX) Bfloat16 and Integer8.
+|Deep Learning| Intel® Extension for PyTorch (IPEX)| [PyTorch Inference Optimizations with Intel® Advanced Matrix Extensions (Intel® AMX) Bfloat16 Integer8](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/README.md)   | Analyze inference performance improvements using Intel® Extension for PyTorch (IPEX) with Advanced Matrix Extensions (Intel® AMX) Bfloat16 and Integer8.
 |Deep Learning| PyTorch | [IntelPyTorch TrainingOptimizations Intel® AMX BF16](IntelPyTorch_TrainingOptimizations_AMX_BF16)   | Analyze training performance improvements using Intel® Extension for PyTorch (IPEX) with Intel® AMX Bfloat16.
 |Data Analytics | Numpy, Numba | [IntelPython Numpy Numba dpex kNN](IntelPython_Numpy_Numba_dpex_kNN)   | Optimize k-NN model by numba_dpex operations without sacrificing accuracy.
 |Classical Machine Learning| XGBoost | [IntelPython XGBoost Performance](IntelPython_XGBoost_Performance) |  Analyze the performance benefit from using Intel optimized XGBoost compared to un-optimized XGBoost 0.81.
 |Classical Machine Learning| XGBoost | [IntelPython XGBoost daal4pyPrediction](IntelPython_XGBoost_daal4pyPrediction) |  Analyze the performance benefit of minimal code changes to port pre-trained XGBoost model to daal4py prediction for much faster prediction than XGBoost prediction.
 |Classical Machine Learning| daal4py | [IntelPython daal4py DistributedKMeans](IntelPython_daal4py_DistributedKMeans)    | Train and predict with a distributed k-means model using the python API package daal4py powered by the oneAPI Data Analytics Library.
 |Classical Machine Learning| daal4py | [IntelPython daal4py DistributedLinearRegression](IntelPython_daal4py_DistributedLinearRegression)    | Run a distributed Linear Regression model with oneAPI Data Analytics Library (oneDAL) daal4py library memory objects.
-|Deep Learning| PyTorch | [IntelPytorch Interactive Chat Quantization](IntelPytorch_Interactive_Chat_Quantization)   | Create interactive chat based on pre-trained DialoGPT model and add the Intel® Extension for PyTorch (IPEX) quantization to it.
-|Deep Learning| PyTorch | [IntelPytorch Quantization](IntelPytorch_Quantization)   | Inference performance improvements using Intel® Extension for PyTorch (IPEX) with feature quantization.
+|Deep Learning| PyTorch | [Interactive Chat Based on DialoGPT Model Using Intel® Extension for PyTorch* Quantization](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/README.md)   | Create interactive chat based on pre-trained DialoGPT model and add the Intel® Extension for PyTorch (IPEX) quantization to it.
+|Deep Learning| PyTorch | [Optimize PyTorch Models using Intel® Extension for PyTorch* (IPEX) Quantization](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/README.md)   | Inference performance improvements using Intel® Extension for PyTorch (IPEX) with feature quantization.
 |Deep Learning| TensorFlow | [IntelTensorFlow Intel® AMX BF16 Training](IntelTensorFlow_AMX_BF16_Inference) | Enabling auto-mixed precision to use low-precision datatypes, like bfloat16, for model inference with TensorFlow* .
 |Deep Learning| TensorFlow | [IntelTensorFlow Intel® AMX BF16 Training](IntelTensorFlow_AMX_BF16_Training) | Training performance improvements with Intel® AMX BF16.
 |Deep Learning| TensorFlow | [IntelTensorFlow Enabling Auto Mixed Precision for TransferLearning](IntelTensorFlow_Enabling_Auto_Mixed_Precision_for_TransferLearning) | Enabling auto-mixed precision to use low-precision datatypes, like bfloat16, for transfer learning with TensorFlow*.