With the abundance of deep learning frameworks available today, it can be difficult to know what to choose for any particular application. Given the contrasting strengths and weaknesses of these frameworks, the ability to work with and switch between more than one is particularly important. Recent Cloudera blogs have shown how examples of applying deep learning on the Cloudera ecosystem using popular frameworks Deeplearning4j, BigDL, and Keras+TensorFlow.
In the past few years, deep learning has seen incredible success in image recognition applications. In this post I examine how to train a convolutional neural network to recognize playing card images from a game called SET®, explore the structure of the model to get some insight into what it is “seeing”, and present a webcam application that uses the deployed model in a near-realtime setting.
SET is a card game where the objective is to find triples of cards,
Alas! what a great loss there will be to learning
before the cycle of the Moon is completed.
“The Prophecies of Nostradamus“, Century I, 62
In 1555, did Nostradamus predict deep learning built on stochastic optimization using a loss function’s gradient? Almost certainly not, but, what can deep learning predict about Nostradamus in 2017?