llSourcell/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
repo name | llSourcell/How_to_use_Tensorflow_for_classification-LIVE |
repo link | https://github.com/llSourcell/How_to_use_Tensorflow_for_classification-LIVE |
homepage | |
language | Jupyter Notebook |
size (curr.) | 7 kB |
stars (curr.) | 75 |
created | 2017-01-25 |
license | |
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
This is the code for this 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
- matplotlib
- tensorflow
- pandas
- numpy
Install dependencies using pip Install jupyter notebook using this
##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 for the code go to jalammar. I’ve merely created a wrapper to get people started.