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Digests » 10
this week's favorite
Simple step-by-step walkthroughs to solve common machine learning problems using best practices.
I read a lot of deep learning papers, typically a few/week. I've read probably several thousands of papers. My general problem with papers in machine learning or deep learning is that often they sit in some strange no man's land between science and engineering, I call it "academic engineering".
There's a real joy to capturing slow-motion video of anything, whether it's a great sports moment, a hilarious surprise, or a rock-solid punch. The trouble is knowing ahead of time that something slow-motion-worthy is about to happen, since converting regular video into slow-mo satisfaction is a choppy process.
X-ray vision has long seemed like a far-fetched sci-fi fantasy, but over the last decade a team led by Professor Dina Katabi from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has continually gotten us closer to seeing through walls.
To announce Google’s AutoML, Google CEO Sundar Pichai wrote, “Today, designing neural nets is extremely time intensive, and requires an expertise that limits its use to a smaller community of scientists and engineers.