how_to_do_math_for_deep_learning

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how_to_do_math_for_deep_learning

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

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# 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](https://youtu.be/N4gDikiec8E) 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](http://jupyter.readthedocs.io/en/latest/install.html)

## 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](https://www.kaggle.com/usgs/earthquake-database) 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](http://iamtrask.github.io/2015/07/12/basic-python-network/). I've merely created a wrapper to get people started.

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