動的計画法によるDVDのディスク分割の改善
こんにちは。「家族アルバム みてね」の開発チームに所属している黒川と申します。今回は、その「みてね」の機能の1つで、写真や動画をDVDにして注文できる機能を動的計画法を使って改善した話をします。 「みてね」では家族の写真や動画をアップロードし、アプリ上で月ごとに振り返ることが可能になっています。一方、たとえば自宅のテレビやパソコンでまとめて振り返りたいという要望もあり、「みてね」では最長過去1年間の写真や動画をDVDにまとめて注文することができます。 このときに問題となるのがDVDのディスク分割です。1年分の写真や動画はともすると1枚のディスクに収まりきらず、複数のディスクに分割する必要があります。いままでは、動画を月ごとに分けて各ディスクに入れていく、というシンプルなアルゴリズムで分割を行っていました。しかし、ユーザーさんからは「1枚のディスクにすこしの動画しかないがどうなっているのか」
Convolutional Methods for Text
tl;drRNNS work great for text but convolutions can do it fasterAny part of a sentence can influence the semantics of a word. For that reason we want our network to see the entire input at onceGetting that big a receptive can make gradients vanish and our networks failWe can solve the vanishing gradient problem with DenseNets or Dilated ConvolutionsSometimes we need to generate text. We can use “de
fast.ai: How I built a deep learning application to detect invasive species in just 1 day (and for…
I’ve read dozens of books and taken many classes on machine learning. fast.ai is, by far, the most practical way to get started with deep learning. In this post I’ll share my experience after completing the first 2 lessons. Trust me — starting with math is a bad way to learn AI22 years ago I sat in Bernie Widrow’s Neural Networks class at Stanford and watched the father of the digital filter pains
Neural Network Architectures
Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. For a more in-depth analysis and comparison of all the networks reported here, please see our recent article (and upda
Linear algebra cheat sheet for deep learning
During Jeremy Howard’s excellent deep learning course I realized I was a little rusty on the prerequisites and my fuzziness was impacting my ability to understand concepts like backpropagation. I decided to put together a few wiki pages on these topics to improve my understanding. Here is a very basic intro to some of the more common linear algebra operations used in deep learning. NEW: check out
NanoNets : How to use Deep Learning when you have Limited Data
Disclaimer: I’m building nanonets.com to help build ML with less data Part 2 : Building Object Detection Models with Almost no HardwareI think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. If you have a large engine and a tiny amount of fuel, you won’t make it to orbit. If you have a tiny engine and a ton of fuel, you can’t even lift off. To build a rocket you nee
6 areas of AI and machine learning to watch closely
Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”. This is in part because AI is not one technology. It is in fact a broad field constituted of many disciplines, ra
Game Theory reveals the Future of Deep Learning
Credit: https://unsplash.com/search/game?photo=B4op5oZ4x5QIf you’ve been following my articles up to now, you’ll begin to perceive, what’s apparent to many advanced practitioners of Deep Learning (DL), is the emergence of Game Theoretic concepts in the design of newer architectures. This makes intuitive sense for two reasons. The first intuition is that DL systems will eventually need to tackle si
Deep Learning the Stock Market
Update 25.1.17 — Took me a while but here is an ipython notebook with a rough implementation In the past few months I’ve been fascinated with “Deep Learning”, especially its applications to language and text. I’ve spent the bulk of my career in financial technologies, mostly in algorithmic trading and alternative data services. You can see where this is going. I wrote this to get my ideas straight
Deep Learning in Rust: a walk in the park
This is a sequel to my previous post several months ago. Last time, I introduced a shoddy library named deeplearn-rs that allowed you to build and (manually) train neural networks that run on your GPU via OpenCL. I told you to “take a gander” at some atrocious code that constructed a rather useless XOR network and then didn’t bother to train it. For reasons unknown, I still got positive feedback.
Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines
(An alternate version of this article was originally published in the Boston Globe) On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence
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