Neural Networks Demystified [Part 7: Overfitting, Testing, and Regularization]

2015-01-16

[public] 137K views, 4.29K likes, 16.0 dislikes audio only

We've built and trained our neural network, but before we celebrate, we must be sure that our model is representative of the real world.

Supporting Code:

https://github.com/stephencwelch/Neural-Networks-Demystified

Nate Silver's Book: http://www.amazon.com/Signal-Noise-Many-Predictions-Fail/dp/159420411X/ref=sr_1_1?ie=UTF8&qid=1421442340&sr=8-1&keywords=signal+and+the+noise

Caltech Machine Learning Course: https://work.caltech.edu/telecourse.html

And the lecture shown: http://youtu.be/Dc0sr0kdBVI?t=56m52s

In this series, we will build and train a complete Artificial Neural Network in python. New videos every other friday.

Part 1: Data + Architecture

Part 2: Forward Propagation

Part 3: Gradient Descent

Part 4: Backpropagation

Part 5: Numerical Gradient Checking

Part 6: Training

Part 7: Overfitting, Testing, and Regularization

@stephencwelch

welchlabs.com