fix sample.json error and use full name of INC

Author: NeoZhangJianyu

AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/README.md

@@ -1,15 +1,15 @@
-# `Intel®  Neural Compressor (Intel® INC)` Sample for TensorFlow*
+# `Intel® Neural Compressor` Sample for TensorFlow*
 
 ## Background
 Low-precision inference can speed up inference obviously, by converting the fp32 model to int8 or bf16 model. Intel provides Intel® Deep Learning Boost technology in the Second Generation Intel® Xeon® Scalable Processors and newer Xeon®, which supports to speed up int8 and bf16 model by hardware.
 
-Intel® Low Precision Optimization Tool (Intel INC) helps the user to simplify the processing to convert the fp32 model to int8/bf16.
+Intel® Neural Compressor helps the user to simplify the processing to convert the fp32 model to int8/bf16.
 
-At the same time, Intel INC will tune the quanization method to reduce the accuracy loss, which is a big blocker for low-precision inference.
+At the same time, Intel® Neural Compressor will tune the quanization method to reduce the accuracy loss, which is a big blocker for low-precision inference.
 
-Intel INC is released in Intel® AI Analytics Toolkit and works with Intel® Optimization of TensorFlow*.
+Intel® Neural Compressor is released in Intel® AI Analytics Toolkit and works with Intel® Optimization of TensorFlow*.
 
-Please refer to the official website for detailed info and news: [https://github.com/intel/lp-opt-tool](https://github.com/intel/lp-opt-tool)
+Please refer to the official website for detailed info and news: [https://github.com/intel/neural-compressor](https://github.com/intel/neural-compressor)
 
 ## License
 
@@ -19,18 +19,18 @@ Code samples are licensed under the MIT license. See
 Third party program Licenses can be found here: [third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt)
 
 ## Purpose
-This sample will show a whole process to build up a CNN model to recognize handwriting number and speed up it by Intel INC.
+This sample will show a whole process to build up a CNN model to recognize handwriting number and speed up it by Intel® Neural Compressor.
 
-We will learn how to train a CNN model based on Keras with TensorFlow, use Intel INC to quantize the model and compare the performance to understand the benefit of Intel INC.
+We will learn how to train a CNN model based on Keras with TensorFlow, use Intel® Neural Compressor to quantize the model and compare the performance to understand the benefit of Intel® Neural Compressor.
 
 ## Key Implementation Details
 
 - Use Keras on TensorFlow to build and train the CNN model.
 
 
-- Define function and class for Intel INC to quantize the CNN model.
+- Define function and class for Intel® Neural Compressor to quantize the CNN model.
 
-  The Intel INC can run on any Intel® CPU to quantize the AI model.
+  The Intel® Neural Compressor can run on any Intel® CPU to quantize the AI model.
 
   The quantized AI model has better inference performance than the FP32 model on Intel CPU.
 
@@ -47,7 +47,7 @@ We will learn how to train a CNN model based on Keras with TensorFlow, use Intel
 | OS                                | Linux* Ubuntu* 18.04 
 | Hardware                          | The Second Generation Intel® Xeon® Scalable processor family or newer
 | Software                          | Intel® oneAPI AI Analytics Toolkit 2021.1 or newer
-| What you will learn               | How to use Intel INC tool to quantize the AI model based on TensorFlow and speed up the inference on Intel® Xeon® CPU
+| What you will learn               | How to use Intel® Neural Compressor tool to quantize the AI model based on TensorFlow and speed up the inference on Intel® Xeon® CPU
 | Time to complete                  | 10 minutes
 
 ## Running Environment
@@ -127,7 +127,7 @@ conda activate tensorflow
             
 ```
 
-### Install Intel INC by Local Channel
+### Install Intel® Neural Compressor by Local Channel
 
 ```
 conda install -c ${ONEAPI_ROOT}/conda_channel neural-compressor -y --offline

AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/sample.json

@@ -14,7 +14,7 @@
 		"env": ["source ${ONEAPI_ROOT}/setvars.sh --force",
 			"conda env remove -n user_tensorflow",
 			"conda create -n user_tensorflow -c ${ONEAPI_ROOT}/conda_channel python=`python -V| awk '{print $2}'` -y",
-			"conda activate user_tensorfinclow",
+			"conda activate user_tensorflow",
                         "conda install -n user_tensorflow -c ${ONEAPI_ROOT}/conda_channel tensorflow python-flatbuffers -y",
                         "conda install -n user_tensorflow -c ${ONEAPI_ROOT}/conda_channel neural-compressor -y --offline",
                         "conda install -n user_tensorflow -c ${ONEAPI_ROOT}/conda_channel lpot -y --offline",

AI-and-Analytics/Getting-Started-Samples/README.md

@@ -17,7 +17,7 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 | Compoment      | Folder                                             | Description
 | --------- | ------------------------------------------------ | -
 | daal4py | [IntelPython_daal4py_GettingStarted](IntelPython_daal4py_GettingStarted)                     | Batch linear regression using the python API package daal4py from oneAPI Data Analytics Library (oneDAL) .
-| INC | [INC-Sample-for-Tensorflow](INC-Sample-for-Tensorflow)                     |Quantize a fp32 model into int8 by Intel®  Neural Compressor (INC), and compare the performance between fp32 and int8 .
+| Intel® Neural Compressor | [INC-Sample-for-Tensorflow](INC-Sample-for-Tensorflow)                     |Quantize a fp32 model into int8 by Intel® Neural Compressor, and compare the performance between fp32 and int8 .
 | Modin | [IntelModin_GettingStarted](IntelModin_GettingStarted)                     | Run Modin-accelerated Pandas functions and note the performance gain .
 | PyTorch | [IntelPyTorch_GettingStarted](IntelPyTorch_GettingStarted) | A simple training example for PyTorch.
 | TensorFlow | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted)               | A simple training example for TensorFlow.