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
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
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
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
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
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
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
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
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.
(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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