fitrialif / how_to_do_math_for_deep_learning

This is the code for "How to Do Math Easily - Intro to Deep Learning #4' by Siraj Raval on YouTube

how_to_do_math_for_deep_learning

This is the code for "How to Do Math Easily - Intro to Deep Learning #4' by Siraj Raval on YouTube

##Overview

This is the code for this video on Youtube by Siraj Raval apart of the 'Intro to Deep Learning' Udacity nanodegree course. We build a 3 layer feedforward neural network trains on a set of binary number input data and predict the binary number output.

##Dependencies

None!

Install Jupyter notebook from here

##Usage

You can either run the notebook by typing jupyter notebook into terminal when in the directory or run the demo.py script by running python demo.py in terminal.

##Weekly Challenge

The challenge for this video is to build a neural network to predict the magnitude of an Earthquake given the date, time, Latitude, and Longitude as features. This is the dataset. Optimize at least 1 hyperparameter using Random Search. See [this](http://scikit learn.org/stable/auto_examples/model_selection/randomized_search.html) example for more information.

You can use any library you like, bonus points are given if you do this using only numpy.

#Due Date: Thursday, February 9th at 12 PM PST

##Credits

Credits for the original code go to Andrew Trask. I've merely created a wrapper to get people started.