2014-12-19
[public] 169K views, 1.71K likes, 19.0 dislikes audio only
When building complex systems like neural networks, checking portions of your work can save hours of headache. Here we'll check our gradient computations.
Supporting code:
https://github.com/stephencwelch/Neural-Networks-Demystified
Link to excellent Stanford tutorial: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
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