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Copy file name to clipboardExpand all lines: AI-and-Analytics/Getting-Started-Samples/README.md
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|Classical Machine Learning| Intel® Optimization for XGBoost* | [IntelPython_XGBoost_GettingStarted](IntelPython_XGBoost_GettingStarted) | Set up and trains an XGBoost* model on datasets for prediction.
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|Classical Machine Learning| daal4py | [IntelPython_daal4py_GettingStarted](IntelPython_daal4py_GettingStarted) | Batch linear regression using the Python API package daal4py from oneAPI Data Analytics Library (oneDAL).
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|Deep Learning <br/> Inference Optimization| Intel® Optimization for TensorFlow* | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted) | A simple training example for TensorFlow.
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|Deep Learning <br/> Inference Optimization|Intel® Extension of PyTorch | [IntelPyTorch_GettingStarted](https://github.com/intel/intel-extension-for-pytorch/tree/main/examples/cpu/inference/python/jupyter-notebooks)| A simple training example for Intel® Extension of PyTorch.
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|Deep Learning <br/> Inference Optimization|Intel® Extension of PyTorch | [IntelPyTorch_GettingStarted]([https://github.com/intel/intel-extension-for-pytorch/tree/main/examples/cpu/inference/python/jupyter-notebooks](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IPEX_Getting_Started.ipynb)| A simple training example for Intel® Extension of PyTorch.
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|Classical Machine Learning| Scikit-learn (OneDAL) | [Intel_Extension_For_SKLearn_GettingStarted](Intel_Extension_For_SKLearn_GettingStarted) | Speed up a scikit-learn application using Intel oneDAL.
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|Deep Learning <br/> Inference Optimization|Intel® Extension of TensorFlow | [Intel® Extension For TensorFlow GettingStarted](Intel_Extension_For_TensorFlow_GettingStarted) | Guides users how to run a TensorFlow inference workload on both GPU and CPU.
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|Deep Learning Inference Optimization|oneCCL Bindings for PyTorch |[Intel oneCCL Bindings For PyTorch GettingStarted](Intel_oneCCL_Bindings_For_PyTorch_GettingStarted)| Guides users through the process of running a simple PyTorch* distributed workload on both GPU and CPU. |
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