Three Tricks to Amplify Small Data for Deep Learning

It's no secret that deep learning lets data science practitioners reach new levels of accuracy with predictive models. However, one of the drawbacks of deep learning is it typically requires huge data sets (not to mention big clusters). But with a little skill, practitioners with smaller data sets can still partake of deep learning riches. Deep learning has exploded in popularity, with good reason: Deep learning approaches, such as convolutional neural networks for computer (used primarily for image data) and recurrent neural networks (used primarily for language and textual data) can deliver higher accuracy and precision compared to "classical" machine learning approaches, like regression algorithms, gradient-boosted trees, and support vector machines. But that higher accuracy comes at a cost.