2023.1 AI Kit README Updates

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

@@ -16,16 +16,21 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 
 | Compoment      | Folder                                             | Description
 | --------- | ------------------------------------------------ | -
-| Scikit-learn | [IntelScikitLearn_Extensions_SVC_Adult](IntelScikitLearn_Extensions_SVC_Adult)   | Use Intel® Extension for Scikit-learn to accelerate the training and prediction with SVC algorithm on Adult dataset. Compare the performance of SVC algorithm optimized through Intel® Extension for Scikit-learn against original Scikit-learn.
-| daal4py | [IntelPython_daal4py_DistributedLinearRegression](IntelPython_daal4py_DistributedLinearRegression)    | Run a distributed Linear Regression model with oneAPI Data Analytics Library (oneDAL) daal4py library memory objects.
-| PyTorch | [IntelPyTorch_Extensions_AutoMixedPrecision](IntelPyTorch_Extensions_AutoMixedPrecision)   | Download, compile, and get started with Intel® Extension for PyTorch*.
-| PyTorch | [IntelPyTorch_TrainingOptimizations_AMX_BF16](IntelPyTorch_TrainingOptimizations_AMX_BF16)   | Analyze training performance improvements using Intel® Extension for PyTorch with Advanced Matrix Extensions Bfloat16.
-| PyTorch | [IntelPyTorch_TorchCCL_Multinode_Training](IntelPyTorch_TorchCCL_Multinode_Training)   | Perform distributed training with oneAPI Collective Communications Library (oneCCL) in PyTorch.
-| TensorFlow & Model Zoo | [IntelTensorFlow_ModelZoo_Inference_with_FP32_Int8](IntelTensorFlow_ModelZoo_Inference_with_FP32_Int8)               | Run ResNet50 inference on Intel's pretrained FP32 and Int8 model.
-| TensorFlow & Model Zoo | [IntelTensorFlow_PerformanceAnalysis](IntelTensorFlow_PerformanceAnalysis) | Analyze the performance difference between Stock Tensorflow and Intel Tensorflow.
-| TensorFlow | [IntelTensorFlow_InferenceOptimization](IntelTensorFlow_InferenceOptimization) |  Optimize a pre-trained model for a better inference performance.
-| XGBoost | [IntelPython_XGBoost_Performance](IntelPython_XGBoost_Performance) |  Analyze the performance benefit from using Intel optimized XGBoost compared to un-optimized XGBoost 0.81.
-| 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..
+| PyTorch | [IntelPyTorch Extensions Inference Optimization](IntelPyTorch_Extensions_Inference_Optimization)   | Applying IPEX Optimizations to a PyTorch workload to gain performance boost.
+| PyTorch | [IntelPyTorch TrainingOptimizations AMX BF16](IntelPyTorch_TrainingOptimizations_AMX_BF16)   | Analyze training performance improvements using Intel® Extension for PyTorch with Advanced Matrix Extensions Bfloat16.
+| Numpy, Numba | [IntelPython Numpy Numba dpex kNN](IntelPython_Numpy_Numba_dpex_kNN)   | Optimize k-NN model by numba_dpex operations without sacrificing accuracy.
+| XGBoost | [IntelPython XGBoost Performance](IntelPython_XGBoost_Performance) |  Analyze the performance benefit from using Intel optimized XGBoost compared to un-optimized XGBoost 0.81.
+| 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.
+| 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.
+| daal4py | [IntelPython daal4py DistributedLinearRegression](IntelPython_daal4py_DistributedLinearRegression)    | Run a distributed Linear Regression model with oneAPI Data Analytics Library (oneDAL) daal4py library memory objects.
+| PyTorch | [IntelPytorch Quantization](IntelPytorch_Quantization)   | Inference performance improvements using Intel® Extension for PyTorch* (IPEX) with feature quantization.
+| TensorFlow | [IntelTensorFlow AMX BF16 Training](IntelTensorFlow_AMX_BF16_Training) | Training performance improvements with Intel® AMX BF16.
+| 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*.
+| TensorFlow | [IntelTensorFlow InferenceOptimization](IntelTensorFlow_InferenceOptimization) |  Optimize a pre-trained model for a better inference performance.
+| TensorFlow & Model Zoo | [IntelTensorFlow ModelZoo Inference with FP32 Int8](IntelTensorFlow_ModelZoo_Inference_with_FP32_Int8)               | Run ResNet50 inference on Intel's pretrained FP32 and Int8 model.
+| TensorFlow & Model Zoo | [IntelTensorFlow PerformanceAnalysis](IntelTensorFlow_PerformanceAnalysis) | Analyze the performance difference between Stock Tensorflow and Intel Tensorflow.
+| TensorFlow | [IntelTensorFlow Transformer AMX bfloat16 MixedPrecisiong](IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision) | Run a transformer classification model with bfloat16 mixed precision.
+| Scikit-learn | [IntelScikitLearn Extensions SVC Adult](IntelScikitLearn_Extensions_SVC_Adult)   | Use Intel® Extension for Scikit-learn to accelerate the training and prediction with SVC algorithm on Adult dataset. Compare the performance of SVC algorithm optimized through Intel® Extension for Scikit-learn against original Scikit-learn..
 
 # Using Samples in Intel® DevCloud for oneAPI
 To get started using samples in the DevCloud, refer to [Using AI samples in Intel® DevCloud for oneAPI](https://github.com/intel-ai-tce/oneAPI-samples/tree/devcloud/AI-and-Analytics#using-samples-in-intel-oneapi-devcloud).