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Top /r/MachineLearning Posts, October: Machine learning video course, neural nets evaluate selfies


Machine learning video lectures, deep nets evaluate selfies, Google focusing on machine learning, DeepMind's huge text dataset made available, implement a recurrent neural net, and open source face recognition with Google's FaceNet.



In November on /r/MachineLearning, we are introduced to a machine learning video course, find out what neural nets think of our selfies, hear about Google's future plans, obtain large datasets, build recurrent neural nets, and check out an open source face recognition project.

1. Excellent YouTube Playlist Explaining Machine Learning Concepts +266

This is a link to a video playlist on mathematicalmonk's YouTube channel, with all video content self-produced. The list includes 100 (!!!) videos organized into 14 lessons, with each video running, for the most part, between 10 and 15 minutes. Given the user's handle, it makes sense that the videos seem to have a mathematical bent, which also makes sense given that the content focuses on the theoretical. I checked out a cross-validation videos out of curiousity and was impressed. Disclaimer: bring your probability skills.

Cross Validation Video Capture

2. What a Deep Neural Network Thinks About Your #selfie +204

Andrej Karpathy trained a deep neural network to recognize good and bad selfies, with the ultimate result being a Twitter bot that can accept a tweeted photo and rate your submission. Karpathy describes his clever selfie scraping and training method, and writes the entire ordeal up in a brilliant article. I recommend reading his full post; however, if time constraints prevent you, you can check out my summary here.

3. Google 'Rethinking Everything' Around Machine Learning +190

Google is doing well. Apparently. Despite this unsurprising bit of financial news, CEO Sundar Pichai says that Google is rethinking everything around the continued successes of machine learning. "Machine learning is a core, transformative way by which we're rethinking everything we're doing," says Pichai. This certainly fits with Gartner's recent revelation, but it's hardly shocking. Google's bread and butter is algorithms, and they've been learning machines for a very long time.

4. DeepMind's Huge Machine Reading Question/Answer Dataset Available to Public +165

From Google Deepmind comes a Github repo of scripts helping to address the shortage of machine reading system datasets for testing their abilities to answer questions posed on the contents of documents that they have "read." Originally utilized in this paper by Hermann, et al., this Python script generates question/answer pairs from CNN and Daily Mail articles obtained from the Wayback Machine.

5. How to Implement a Recurrent Neural Network Part 1 +145

This is part 1 of a post by Peter Roelants on implementing a recurrent neural network (RNN). This first part covers a simple RNN, backpropagation, and resilient backprop (Rprop) optimisation. Peter has also written a previous tutorial covering the basics of neural networks, which may be a good starting point. You can get the iPython notebook of the RNN tutorial here.

6. Nice Course on Machine Learning +136

This is mathematicalmonk's second appearance in the top subreddit's post for October. Same course, same topics, but this links directly to video number 1.

7. OpenFace: Face Recognition with Google's FaceNet Deep Neural Network +128

This is a Github repo containing a Python and Torch implementation of the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering. This paper was written at Google, using publicly available libraries and datasets, by Florian Schroff, Dmitry Kalenichenko, and James Philbin. The network can run on both GPU and CPU. This is yet another example of the "deep learning arXiv paper to open source Github implementation cycle."

Bio: Matthew Mayo is a computer science graduate student currently working on his thesis parallelizing machine learning algorithms. He is also a student of data mining, a data enthusiast, and an aspiring machine learning scientist.

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