Fooling Neural Networks

Rodrigo Agundez shows how to fool neural networks:

A comprehensive and complete summary can be found in the When DNNs go wrong blog, which I recommend you to read.

All these amazing studies use state of the art deep learning techniques, which makes them (in my opinion) difficult to reproduce and to answer questions we might have as non-experts in this subject.

My intention in this blog is to bring the main concepts down to earth, to an easily reproducible setting where they are clear and actually visible. In addition, I hope this short blog can provide a better understanding of the limitations of discriminative models in general. The complete code used in this blog post can be found here.

This is a great article.

Related Posts

The Whys Of Azure ML Workbench

Ginger Grant explains why Azure Machine Learning Workbench exists: Microsoft is looking for Azure Machine Learning Workbench for more than a tool to use for Machine Learning analysis. It is part of a system to manage and monitor the deployment of machine learning solutions with Azure Machine Learning Model Management. The management aspects are part […]

Read More

Markov Chains In Python

Sandipan Dey shows off various uses of Markov chains as well as how to create one in Python: Perspective. In the 1948 landmark paper A Mathematical Theory of Communication, Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today’s Information Age. In this paper, Shannon proposed using a Markov chain to […]

Read More


November 2017
« Oct Dec »