update README.md for some missing links (oneapi-src#824)

* update README.md for some missing links

* Update README.md

* update README.md according to review

Author: louie-tsai

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

@@ -18,14 +18,17 @@ This sample helps demonstrate AI workloads and deep learning models optimized by
 ## Key implementation details
 ### Jupyter Notebooks 
 
-These Jupyter notebooks help users analyze the performance benefit from using Intel Optimizations for Tensorflow with the oneDNN library.
+Jupyter notebooks help users analyze the performance benefit from using Intel Optimizations for Tensorflow with the oneDNN library.  
+
+>The notebooks are released with oneAPI AI Analytics Toolkit, and they are under the /opt/intel/oneapi/modelzoo/latest/models/docs/notebooks/perf_analysis folder.  
+Users could also find the notebooks in Model Zoo Github by following the links in the below table.  
 
 | Analysis Type | Notebook | Notes|
 | ------ | ------ | ------ |
-|stock vs. Intel Tensorflow | 1. [benchmark_perf_comparison](benchmark_perf_comparison.ipynb)  | Compare performance between Stock and Intel Tensorflow among different models  |
-|^| 2. [benchmark_perf_timeline_analysis](benchmark_perf_timeline_analysis.ipynb) | Analyze the performance benefit from oneDNN among different layers by using Tensorflow Timeline |  
-|fp32 vs. bf16 vs. int8 | 1. [benchmark_data_types_perf_comparison](benchmark_data_types_perf_comparison.ipynb) | Compare Model Zoo benchmark performance among different data types on Intel Optimizations for  Tensorflow  |
-|^| 2.[benchmark_data_types_perf_timeline_analysis](benchmark_data_types_perf_timeline_analysis.ipynb) | Analyze the bf16/int8 data type performance benefit from oneDNN among different layers by using Tensorflow Timeline |  
+|stock vs. Intel Tensorflow | 1. [benchmark_perf_comparison](https://github.com/IntelAI/models/blob/master/docs/notebooks/perf_analysis/benchmark_perf_comparison.ipynb) | Compare performance between Stock and Intel Tensorflow among different models  |
+|^| 2. [benchmark_perf_timeline_analysis](https://github.com/IntelAI/models/blob/master/docs/notebooks/perf_analysis/benchmark_perf_comparison.ipynb) | Analyze the performance benefit from oneDNN among different layers by using Tensorflow Timeline |  
+|fp32 vs. bf16 vs. int8 | 1. [benchmark_data_types_perf_comparison](https://github.com/IntelAI/models/blob/master/docs/notebooks/perf_analysis/benchmark_data_types_perf_comparison.ipynb) | Compare Model Zoo benchmark performance among different data types on Intel Optimizations for  Tensorflow  |
+|^| 2.[benchmark_data_types_perf_timeline_analysis](https://github.com/IntelAI/models/blob/master/docs/notebooks/perf_analysis/benchmark_data_types_perf_timeline_analysis.ipynb) | Analyze the bf16/int8 data type performance benefit from oneDNN among different layers by using Tensorflow Timeline |  
 
 
 ## License  
@@ -55,7 +58,7 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 
 1. Create conda env: `$conda create -n stock-tensorflow python matplotlib ipykernel psutil pandas gitpython`
 2. Activate the created conda env: `$source activate stock-tensorflow.`
-3. Install stock Tensorflow with a specific version: `(stock-tensorflow) $pip install tensorflow==2.5.0`
+3. Install stock Tensorflow with a specific version: `(stock-tensorflow) $pip install tensorflow==2.6.0`
 4. Install extra needed package: `(stock-tensorflow) $pip install cxxfilt`
 5. Deactivate conda env: `(stock-tensorflow)$conda deactivate`
 6. Register the kernel to Jupyter NB: `$~/.conda/envs/stock-tensorflow/bin/python -m ipykernel install --user --name=stock-tensorflow`
@@ -85,7 +88,7 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 1. Create conda env: `$conda create -n stock-tensorflow python matplotlib ipykernel psutil pandas gitpython`
 
 2. Activate the created conda env: `$conda activate stock-tensorflow`
-3. Install stock tensorflow with a specific version: `(stock-tensorflow) $pip install tensorflow==2.5.0`
+3. Install stock tensorflow with a specific version: `(stock-tensorflow) $pip install tensorflow==2.6.0`
 4. Install extra needed package: `(stock-tensorflow) $pip install cxxfilt`
 5. Deactivate conda env: `(stock-tensorflow)$conda deactivate`
 6. Register the kernel to Jupyter NB: `$~/anaconda3/envs/stock-tensorflow/bin/python  -m ipykernel install --user --name=stock-tensorflow`