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Top /r/MachineLearning Posts, Mar 22-28: Deep Learning flaws & Security, DeepMind Publications, and Keras


Computer Vision security issues, DeepMind, statistics with Python, hacking on neural networks, and Keras, a neural network library are all topics on top of /r/MachineLearning this week.



Grant Marshall

Fooling images This week on /r/MachineLearning, we have posts ranging from computer vision security to new deep learning libraries.

1. Images that fool computer vision raise security concerns +101

This post details work done at Cornell and University of Wyoming investigating pathological cases where Deep Learning algorithms used for image recognition falsely identify common objects with high confidence. The researchers raise issues that this may cause in security moving forwards. It’s an interesting read if you like security and are interested in its intersection with computer vision and image recognition.

2. Google DeepMind publications all in one place +84

This page includes a nice chronological list of all DeepMind related publications. It also includes the most important publications at the top. If you’ve been interested in getting a comprehensive look at the development of DeepMind, this is a good launching point.

3. Introduction to Statistics using Python +73

This guide, presented in a similar fashion to Python documentation, goes over how to conduct statistical analysis using Python. If you’ve been considering picking up Python for statistics work, this is an excellent guide.

4. Hacker's guide to Neural Networks +65

This is a hacker’s guide to neural networks, meaning that it takes a very example and code oriented approach to teaching about neural networks. It develops the topic from the basics like implementing low-level circuits and functions as code. If you like this type of approach, give this guide a read.

5. Keras: Theano-based deep learning lib, focused on fast prototyping. Supports RNNs and convnets. +62

This Github link leads to the repository for Keras, which is a minimalist and modular deep learning library. It’s based on Theano, but follows a similar mindset as Torch. If you like the idea of Torch, but want to work in the Theano/Python environment instead of Lua, this is a serious alternative to consider.

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