In the last year or so, I have begun working extensively with Keras, Tensorflow and CNTK for various problems at work in industries ranging from manufacturing, to media, to cybersecurity.

Here is a simple convolutional network tutorial on Kaggle that I developed in Keras and Tensorflow. Given the GPU-enabled kernels you have within Kaggle these days, it has become easy enough to train large scale image data on some of these kernels. Performance is another matter, though, since the Tesla K40 GPUs you get here are the lower end GPUs, and are also load balanced for multiple users. In any case, it allows you to even try out CUDA code – and that opportunity can’t be beat, given the low cost of doing Kaggle.

My motivation for putting together a tutorial is not the dearth of tutorials – there are enough and more out there. However I wanted to emphasize certain good practices here, and intend to continue to update the kernel in question in future to illustrate those.

Caveat: The internet is awash with tutorials on deep learning using these frameworks, so I won’t dwell much on why this tutorial is different, because it isn’t very different. That said, it does emphasize how a simple deep learning model could be made more effective by using various good practices, such as batch normalization, some explanations about loss functions, and some amount of data exploration in the context of data and labels for this supervised problem.