Top /r/MachineLearning Posts, Feb 22-28: Jurgen Schmidhuber AMA and Machine Learning Done Wrong

The Jürgen Schmidhuber AMA begins taking questions, machine learning done wrong, GPUs for deep learning, Google opens its native MapReduce capabilities, and Google publishes its DeepMind paper this week on /r/MachineLearning

Grant Marshall

Juergen Schmidhuber This week on /r/MachineLearning, we have a number of great posts from AMAs to GPUs.

1. I am Jürgen Schmidhuber, AMA! +129

This AMA, which was announced a couple weeks ago, is finally here! As of this writing, the post is still open for questions. Jürgen Schmidhuber will then begin answering questions on March 4th. There have already been some great questions posed, and it will surely be a post to revisit once he’s begun answering.

2.  Machine Learning Done Wrong - Some common mistakes +85

This insightful post explores many common mistakes, most of which this author has himself committed or seen at some point. Definitely a good read, especially if you’re newer to data science and machine learning.

3. Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning +75

This very practical guide details what factors to consider when shopping for GPUs to perform deep learning. Given the recent uptick in interest around the topic, think post is worth a read.

4. Google open sources a MapReduce framework for C/C++ +75

As the title implies, this post details Google’s open sourcing of their native framework for MapReduce and Hadoop. If you’re working on a project built on MapReduce, this could unlock some new options in the engineering of your data processing pipeline. If that’s related to your current work, definitely give this a read.

5. Google DeepMind Nature Paper: Human-level control through deep reinforcement learning +70

Here’s another Google-related post. This post links to Google’s newest paper on deep reinforcement learning. If you’re interested in these topics or simply want to keep up with the state-of-the-art, be sure not to miss this paper.