Top /r/MachineLearning Posts, Apr 19-25: Neural nets for nipple detection; NHL Goal celebration hack

Convolutional neural nets and Android App for nipple detection (NSFW), NHL goal detection, Geoff Hinton recent AI talk, top machine learning podcasts, and matrix multiplication in deep learning.

Grant Marshall

NHL Goal Spectrogram This week on /r/MachineLearning, we see a new top post of all time, audio processing for goal detection, slides from a talk by Geoff Hinton, ML podcasts, and how matrix multiplication impacts deep learning performance.

1. Android App: Nipple Detection using Convolutional Neural Network. Results. [NSFW] +625

This is certainly an unconventional, and unusually popular, post on the subreddit. It should be noted that the link is probably not appropriate if you’re at work. The app (which can be found on the Play Store here) provides regions and confidences for nipple detection in pictures taken on or loaded on your phone. If you’re interested in what the author used specifically for this task, they state that they used Caffe. One funny consequence of the popularity of this post is that only a week after the Andrew Ng AMA (the top post on the subreddit at the time), there is a new top post.

2. Epic NHL goal celebration hack: real-time machine learning and Philips hue light show +80

This post details the creation of a system to detect NHL goals and perform a laser light show. It essentially uses signal processing and machine learning on the audio feed of the game to determine when a goal was performed, then interacts with the hardware to change the lighting in the room. This is a really interesting post because of how deep in the software stack it goes, from high-level dataset building down to interfacing between machine learning libraries and hardware.

3. Aetherial Symbols - A (seemingly) new talk by Geoff Hinton +59

This link leads to the slides for a talk on intelligence by Geoff Hinton. They go to a talk given last month at Stanford. Based on the slides, which are absolutely fascinating, I would love to see the actual talk, but it doesn’t seem to be available at this time.

4. I haven't seen a lot of talk about podcasts on here. Here's a list of ML podcasts I enjoy. Hope it's helpful. +58

This post lists a diverse selection of machine learning podcasts. If you want to look through these, keep in mind that they are each fairly different from the last, with some having very different tones and subjects, so listen around a bit before coming to conclusions about the whole list. Personally, I find the talking machines podcast to have good content and a fairly formal, but didactic, tone.

5. Why GEMM is at the heart of deep learning +49

This post investigates performance properties of deep learning in practice. The author finds that most time done in common deep learning situations is spent in GEMM (generalized matrix multiplication). From there, the author looks at how GEMM is done in different contexts. If you’re interested in the under-the-hood performance of deep learning, read this post.