Learning to be Giant.

Website Upgrade

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During the past week, I made some upgrade to this website:

  1. Website currently is Jekyll 3 ready. During hte past several months, the website cannot be updated since Github Pages migrated from Jekyll 2 to Jekyll 3, and the website had some configurations that are not compatible with Jekyll 3.
  2. Stylesheets are refactored to Sass. The original Lanyon template used pure CSS. In this upgrade, I changed it to scss. Therefore, the current template is easier to maintain and customize. All the configuration that are relevant to style is in _config.scss.
  3. Support multilevel menu. The sidebar currently support multilevel menu (however only 2 levels supported).
  4. Better _config.yml. Now _config.yml contains more configuration options. People can get what they want without dive into the code.
  5. Many more…

The source code is https://github.com/codinfox/codinfox-lanyon.

How to compile Caffe

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Compiling caffe is really a pain in the ass. This may be a slightly better (maybe not) version of the Caffe installation instruction. Please check http://caffe.berkeleyvision.org/installation.html for more information. This tutorial is tested on a workstation with Titan Black and Tesla K40. Ubuntu 14.04 Linux is assumed.

First of all, download Caffe source code from Github:

git clone https://github.com/BVLC/caffe.git

Center-Surround Mechanism for Edge Detection

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在研究计算视觉模型的时候,Center-Surround机制是不得不提的,基本上是所有理论的前提。感性理解,Center-Surround机制就是通过对于receptive field当中不同位置对光的不同反应帮助生物视觉系统识别边缘信息。很早之前在做视觉注意(Visual Attention)的时候经常遇到Center-Surround,但是从来没有认真弄懂,只是默认它就等同于LoG(Laplacian of Gaussian)。如今又接触,终于了解清楚,于是记录下来。

React浅析(React Demystified)

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本文翻译自:React Demystified by Josh Haberman,已得到原作本人授权。

我通常在这个博客当中写一些关于parsing和底层编程的内容,但这篇文章跟平时的那些主题不太相关。最近我对诸多JavaScript的框架产生了些兴趣,包括Facebook发布的React。我最近读的一些文章,尤其是The Future of JavaScript MVC Frameworks,让我确信在React当中蕴含着一些非常强大的深层次思想,但是我至今没能找到一篇让我满意的解释React的核心概念的文章。如同之前的LL and LR Parsing Demystified一样,本文将试图用一种我个人能够认可的方式来解释React当中的这些核心思想。

Use RethinkDB to extract distinct tags with Map-reduce

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This is originally a question I asked on StackOverflow, but I came up with the answer by myself.

I have a database whose documents are of the format:

    {
        'timestamp': 1431307846643,
        'tag': 'tag1',
        'message': 'message content'
    }

What I want to do is to:

  1. group all the documents by message
  2. find the earliest and latest timestamp from each group
  3. find all the distinct tags in a group