Top /r/MachineLearning Posts, June: NumPy Gets Funding; ML Cheat Sheets For All; Hot Dog or Not?!?
NumPy receives first ever funding, thanks to Moore Foundation; Cheat Sheets for deep learning and machine learning; How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow & Keras; Andrej Karpathy leaves OpenAI for Tesla; Machine, a machine learning IDE
In June on /r/MachineLearning we learned of funding to a popular (and essential) Python project, are treated to a collection of machine learning cheat sheets, see how deep learning is done on premium cable television, read about Andre Karpathy's new job, and are introduced to a new machine learning "IDE."
The top 5 /r/MachineLearning posts of the past month are:
This is good news for the project. From the news release:
For the first time ever, NumPy — a core project for the Python scientific computing stack — has received grant funding. The proposal, “Improving NumPy for Better Data Science” will receive $645,020 from the Moore Foundation over 2 years, with the funding going to UC Berkeley Institute for Data Science. The principal investigator is Dr. Nathaniel Smith.
Congrats to the project, the project lead, and all of the contributors. The money is great, as is the recognition.
This is the accompanying Github repo for this post by Mate Labs co-founder Kailash Ahirwar. The title is good, but it actually includes cheat sheets for topics and tools beyond machine learning (and deep learning), and ventures into other areas of data science (especially visualization). This is a solid set of reference materials to choose from (I'm particularly a fan of the Data Camp products).
From the article:
The app was developed in-house by the show, by a single developer, running on a single laptop & attached GPU, using hand-curated data. In that respect, it may provide a sense of what can be achieved today, with a limited amount of time & resources, by non-technical companies, individual developers, and hobbyists alike. In that spirit, this article attempts to give a detailed overview of steps involved to help others build their own apps.
What a time to be alive.
Likely old news for most people at this point, but Andrej Karpathy has secured a prestigious position at Tesla. From the TechCrunch article:
Tesla has hired deep learning and computer vision expert Andrej Karpathy in a key Autopilot role. Karpathy most recently held a role as a researcher at OpenAI, the artificial intelligence nonprofit backed by Elon Musk. He has an extensive background in AI-related fields, having completed a PhD at Stanford University in computer vision.
Congrats to Mr. Karpathy. I'm sure Tesla will benefit from his expertise.
This is a video introduction to Machine, a self-described machine learning IDE. While some in the accompanying discussion thread have pointed out that this is not really an IDE, others have focused on the positive by expressing that at least it doesn't look like Weka (not my words, Dr. Witten).
Have a look at the video and judge for yourself. While there may be debate as to whether or not it is actually an "IDE," excitement for this project is very real, and time will tell what sort of impact it may or may not have.
- Top /r/MachineLearning Posts, May: Deep Image Analogy; Stylized Facial Animations; Google Open Sources Sketch-RNN
- Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with Scikit-Learn & TensorFlow
- Top /r/MachineLearning Posts, March: A Super Harsh Guide to Machine Learning; Is it Gaggle or Koogle?!?