## the back-propagation equations for a convolutional network

we derive the backprop equations necessary for training a convolutional neural network

we derive the backprop equations necessary for training a convolutional neural network

we derive the backpropagation equations used to correct the weights for a 3 layer neural net with an arbitrary number of neurons

We create a three layer neural net that can identify handwritten numbers and use a regularization technique called dropout to prevent overfitting

we can best understand how neural nets work by studying a small, simplified example

unsupervised learning algorithms such as Isomap and K-means can produce intuitive categorizations for unlabeled data