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# How_to_use_Tensorflow_for_classification-LIVE
This is the code for the "How to Use Tensorflow for Classification" live session by Siraj Raval on Youtube

##Overview
## Overview

This is the code for [this](https://www.youtube.com/watch?v=4urPuRoT1sE) live session by Siraj Raval on Youtube. We'll build
a classifier for houses. The housing data contains features for each house like # of bathrooms, price, and area. We'll manually add labels to our data (good buy or bad buy) then given a new house, we'll predict whether or not it's a good buy or bad buy. We use gradient descent as our optimization strategy and

##Dependencies
## Dependencies

* matplotlib
* [tensorflow](https://www.tensorflow.org/get_started/os_setup)
Expand All @@ -16,10 +16,10 @@ a classifier for houses. The housing data contains features for each house like
Install dependencies using [pip](https://pip.pypa.io/en/stable/)
Install jupyter notebook using [this](http://jupyter.readthedocs.io/en/latest/install.html)

##Usage
## Usage

type `juptyer notebook` into terminal and a browser window will pop up. Click on demo.ipynb. You can iteratively compile
each block of code to see the output results.

##Credits
## Credits
Credits for the code go to [jalammar](https://github.com/jalammar). I've merely created a wrapper to get people started.