This is the code for "How to Do Math Easily - Intro to Deep Learning #4' by Siraj Raval on YouTube
Jupyter Notebook Python
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README.md

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 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.