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

Performance Tuning Neural Network Training

Sean Owen takes us through a few techniques for speeding up neural network model training: Step #2: Use Early StoppingKeras (and other frameworks) have built-in support for stopping when further training appears to be making the model worse. In Keras, it’s the EarlyStopping callback. Using it means passing the validation data to the training process for evaluation […]

Read More

Text Analysis from Google Sheets

Federico Pascual shows how you can use MonkeyLearn to perform text analysis (including sentiment analysis and categorization) from a Google Sheets spreadsheet: Carrying out a customer survey, for example, can be useful to obtain crucial insights into the overall customer experience of your clients. But the data obtained from these surveys can be incredibly difficult […]

Read More

Categories

November 2017
MTWTFSS
« Oct Dec »
 12345
6789101112
13141516171819
20212223242526
27282930