When building complex systems like neural networks, checking portions of your work can save hours of headache. Here we'll check our gradient computations.
Neural Networks Demystified, Part 4: Backpropagation
Backpropagation as simple as possible, but no simpler. Perhaps the most misunderstood part of neural networks, Backpropagation of errors is the key step that allows ANNs to learn. In this video, I give the derivation and thought processes behind backpropagation using high school level calculus.
Neural Networks Demystified, Part 3: Gradient Descent
This time we'll work on strategies for training our neural network.
Neural Networks Demystified, Part 2: Forward Propagation
In part 2 we'll cover moving inputs across out network, introduce the equations we'll need, and write some code.
Neural Networks Demystified, Part 1: Data and Architecture
In this short 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