Programming language & Framework: Python, TensorFlow or any other Neural Network frameworks.

Hints: Review Stochastic Gradient Descent feature weights update video. Review Jupyter Notebook files $(.$ipynb$)$ files on Canvas to build your own Neural Networks with TensorFlow framework

Project Overview: The goal of this assignment is to know how to update the feature weights by using Stochastic Gradient Descent optimizer and solve practical problems by using selfdesigned Neural Networks.

Tasks:

Part $1$: Stochastic Gradient Descent

Part$1\_$x$\_$y$\_$Values.txt file contains $100$ pairs of $($X$,$ Y$)$ points. You will consider the problem of using a curve to fit those data points. Figure $1$ shows the $100$ data points.

You can use multiple features to draw the curve and fit the $100$ data points. For example, if you use $2$ features, your model should be like:

Y $=$ a $*$ X $+$ b $(1)$

The $2$ features are a and b$.$

If you use $3$ features, your model should be like:

Y $=$ a $*$ X$2+$ b $*$ X $+$ c $(2)$ The $3$ features are a$,$ b and c$.$

You can use as many features as you want by following the rule above.