ENTRIES TAGGED "deep learning"

Four short links: 19 September 2014

Four short links: 19 September 2014

Deep Learning Bibliography, Go Playground, Tweet-a-Program, and Memory Management

  1. Deep Learning Bibliographyan annotated bibliography of recent publications (2014-) related to Deep Learning.
  2. Inside the Go Playground — on safely offering a REPL over the web to strangers.
  3. Wolfram Tweet-a-Program — clever marketing trick, and reminiscent of Perl Golf-style “how much can you fit into how little” contests.
  4. Memory Management Reference — almost all you ever wanted to know about memory management.
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Four short links: 7 August 2014

Four short links: 7 August 2014

Material Design, Stewart's Slack, Sketching in Javascript, and Neural Networks and Deep Learning

  1. Material Design in the Google I/O App (Medium) — steps through design thinking as they put Google’s new design metaphor in place. I’ve been chewing on material design. It brings an internal consistency and logic to the Android world that Apple’s iOS and OS X visual worlds have been losing over the years. How long until web users expect this consistency too?
  2. Stewart and Slack (Wired) — profile of Foo Stewart Butterfield and his shiny Slack startup.
  3. p5js — a new Processing-inspired code-as-sketching in Javascript. Using the original metaphor of a software sketchbook, p5.js has a full set of drawing functionality. However, you’re not limited to your drawing canvas, you can think of your whole browser page as your sketch!
  4. Neural Networks and Deep Learning — a free online book to teach you … well, neural networks and deep learning.
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How to build and run your first deep learning network

Step-by-step instruction on training your own neural network.

NeuralTree

When I first became interested in using deep learning for computer vision I found it hard to get started. There were only a couple of open source projects available, they had little documentation, were very experimental, and relied on a lot of tricky-to-install dependencies. A lot of new projects have appeared since, but they’re still aimed at vision researchers, so you’ll still hit a lot of the same obstacles if you’re approaching them from outside the field.

In this article — and the accompanying webcast — I’m going to show you how to run a pre-built network, and then take you through the steps of training your own. I’ve listed the steps I followed to set up everything toward the end of the article, but because the process is so involved, I recommend you download a Vagrant virtual machine that I’ve pre-loaded with everything you need. This VM lets us skip over all the installation headaches and focus on building and running the neural networks. Read more…

Comments: 8
Four short links: 15 July 2014

Four short links: 15 July 2014

Data Brokers, Car Data, Pattern Classification, and Hogwild Deep Learning

  1. Inside Data Brokers — very readable explanation of the data brokers and how their information is used to track advertising effectiveness.
  2. Elon, I Want My Data! — Telsa don’t give you access to the data that your cars collects. Bodes poorly for the Internet of Sealed Boxes. (via BoingBoing)
  3. Pattern Classification (Github) — collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks.
  4. HOGWILD! (PDF) — the algorithm that Microsoft credit with the success of their Adam deep learning system.
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What is deep learning, and why should you care?

Announcing a new series delving into deep learning and the inner workings of neural networks.

OrganicNeuron

Editor’s note: this post is part of our Intelligence Matters investigation.

When I first ran across the results in the Kaggle image-recognition competitions, I didn’t believe them. I’ve spent years working with machine vision, and the reported accuracy on tricky tasks like distinguishing dogs from cats was beyond anything I’d seen, or imagined I’d see anytime soon. To understand more, I reached out to one of the competitors, Daniel Nouri, and he demonstrated how he used the Decaf open-source project to do so well. Even better, he showed me how he was quickly able to apply it to a whole bunch of other image-recognition problems we had at Jetpac, and produce much better results than my conventional methods.

I’ve never encountered such a big improvement from a technique that was largely unheard of just a couple of years before, so I became obsessed with understanding more. To be able to use it commercially across hundreds of millions of photos, I built my own specialized library to efficiently run prediction on clusters of low-end machines and embedded devices, and I also spent months learning the dark arts of training neural networks. Now I’m keen to share some of what I’ve found, so if you’re curious about what on earth deep learning is, and how it might help you, I’ll be covering the basics in a series of blog posts here on Radar, and in a short upcoming ebook. Read more…

Comments: 5
Four short links: 14 July 2014

Four short links: 14 July 2014

Scanner Malware, Cognitive Biases, Deep Learning, and Community Metrics

  1. Handheld Scanners Attack — shipping and logistics operations compromised by handheld scanners running malware-infested Windows XP.
  2. Adventures in Cognitive Biases (MIT) — web adventure to build your cognitive defences against biases.
  3. Quoc Le’s Lectures on Deep Learning — Machine Learning Summer School videos (4k!) of the deep learning lectures by Google Brain team member Quoc Le.
  4. FLOSS Community Metrics Talks — upcoming event at Puppet Labs in Portland. I hope they publish slides and video!
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Four short links: 10 June 2014

Four short links: 10 June 2014

Trusting Code, Deep Pi, Docker DevOps, and Secure Database

  1. Trusting Browser Code (Tim Bray) — on the fundamental weakness of the ‘net as manifest in the browser.
  2. Deep Learning in the Raspberry Pi (Pete Warden) — $30 now gets you a computer you can run deep learning algorithms on. Awesome.
  3. Announcing Docker Hub and Official Repositories — as Docker went 1.0 and people rave about how they use it, comes this. They’re thinking hard about “integrating into the build ship run loop”, which aligns well with DevOps-enabling tool use.
  4. Apple’s Secure Database for Users (Ian Waring) — excellent breakdown of how Apple have gone out of their way to make their cloud database product safe and robust. They may be slow to “the cloud” but they have decades of experience having users as customers instead of products.
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Four short links: 20 May 2014

Four short links: 20 May 2014

Machine Learning, Deep Learning, Sewing Machines & 3D Printers, and Smart Spoons

  1. Basics of Machine Learning Course Notes — slides and audio from university course. Watch along on YouTube.
  2. A Primer on Deep Learning — a very quick catch-up on WTF this is all about.
  3. 3D Printers Have a Lot to Learn from Sewing MachinesSewing does not create more waste but, potentially, less, and the process of sewing is filled with opportunities for increasing one’s skills and doing it over as well as doing it yourself. What are quilts, after all, but a clever way to use every last scrap of precious fabric? (via Jenn Webb)
  4. Liftware — Parkinson’s-correcting spoons.
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Four short links: 9 April 2014

Four short links: 9 April 2014

Internet of Listeners, Mobile Deep Belief, Crowdsourced Spectrum Data, and Quantum Minecraft

  1. Jasper Projectan open source platform for developing always-on, voice-controlled applications. Shouting is the new swiping—I eagerly await Gartner touting the Internet-of-things-that-misunderstand-you.
  2. DeepBeliefSDK — deep neural network library for iOS. (via Pete Warden)
  3. Microsoft Spectrum Observatory — crowdsourcing spectrum utilisation information. Just open sourced their code.
  4. qcraft — beginner’s guide to quantum physics in Minecraft. (via Nelson Minar)
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Four short links: 24 March 2014

Four short links: 24 March 2014

Google Flu, Embeddable JS, Data Analysis, and Belief in the Browser

  1. The Parable of Google Flu (PDF) — We explore two
    issues that contributed to [Google Flu Trends]’s mistakes—big data hubris and algorithm dynamics—and offer lessons for moving forward in the big data age.
    Overtrained and underfed?
  2. Duktape — a lightweight embeddable Javascript engine. Because an app without an API is like a lightbulb without an IP address: retro but not cool.
  3. Principles of Good Data Analysis (Greg Reda) — Once you’ve settled on your approach and data sources, you need to make sure you understand how the data was generated or captured, especially if you are using your own company’s data. Treble so if you are using data you snaffled off the net, riddled with collection bias and untold omissions. (via Stijn Debrouwere)
  4. Deep Belief Networks in Javascript — just object recognition in the browser. The code relies on GPU shaders to perform calculations on over 60 million neural connections in real time. From the ever-more-awesome Pete Warden.
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