Update README.md

README.md

@@ -44,13 +44,16 @@ You can download the module (raw Python file) from here: [DL-Utility-Module](htt
 ### General-purpose regression module (for tabular dataset)
 I also implemented a general-purpose trainer module (`NN_trainer.py`) for regression task with tabular datasets. The idea is that you can simply read a dataset (e.g. a CSV file), choose the input and target variables, build a densely-connected neural net, train, and predict. The module gives you back a prediction function (trained) which can be used for any further prediction, analytics, or optimization task. 
 
-Check out the module [here](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/utils/NN_trainer.py) and an example notebook here.
+Check out the module [here](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/utils/NN_trainer.py) and an example notebook [here](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Demo_general_purpose_regression_module.ipynb).
 
 ## Notebooks
 
 ### Deep learning vs. linear model
 * We show a nonlinear function approximation task performed by linear model (polynomial degree) and a simple 1/2 hidden layer (densely connected) neural net to illustrate the difference and the capacity of deep neural nets to take advantage of larger datasets ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Function%20approximation%20by%20linear%20model%20and%20deep%20network.ipynb)).
 
+### Demo of a general-purpose regression module
+* We implemented a general-purpose trainer module for regression task with tabular datasets. The idea is that you can simply read a dataset (e.g. a CSV file), choose the input and target variables, build a densely-connected neural net, train, predict, and save the model for deployment. This the demo notebook for that module ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Demo_general_purpose_regression_module.ipynb)).
+
 ### Simple Conv Net
 * [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) image classification using densely connected network and 1/2/3 layer CNNs ([Here is the Notebook](https://github.com/tirthajyoti/Computer_vision/blob/master/Notebooks/Fashion_MNIST_using_CNN.ipynb)).