Visualizing Convolutional Neural Networks with Open-source Picasso

While it's easier than ever to define and train deep neural networks (DNNs), understanding the learning process remains somewhat opaque. Monitoring the loss or classification error during training won't always prevent your model from learning the wrong thing or learning a proxy for your intended classification task. Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. Wisely, the researchers had originally taken 200 photos, 100 photos of tanks and 100 photos of trees. They had used only 50 of each for the training set.