PredicSis Blog Improve your Tensorflow models using PredicSis pre-processing

Even if DNN has huge potential for application to all kinds of problems, it also has some significant issues of its own (as with most other ML algorithms): models will fail to manage uncorrelated features; by design, models will over fit and learn from irrelevant, noisy or rare data; and so on. In this post, we propose to pre-process data to automatically reject uncorrelated features and let DNN work on only statistically relevant and optimised data. Using an Automatic Machine Learning pre-processing algorithm designed by PredicSis, we can improve and accelerate Tensorflow modelling.