https://scotch.io/bar-talk/different-tricks-on-how-to-make-bootstrap-columns-all-the-same-height

Just like this problem
https://social.technet.microsoft.com/Forums/ie/en-US/466acbd2-9304-4714-9250-7669913d9a53/unable-to-pass-arguments-to-another-window-when-windowopen?forum=ieitprocurrentver

popupwindow = window.open();
popupwindow.a = “xxx”;

However, in popupwindow, when use:
window.a shows “undefined”

This happened in IE, but work as expected in Chrome. (window.a shows “xxx” )

My workaround is:

window.a = “xxx”;
popupwindow = window.open();

In popupwindow:
window.opener.a shows “xxx”

I use the homebrew and the pyenv to install python.

When I want to import tkinter, there is a error message:
    import _tkinter # If this fails your Python may not be configured for Tk
ImportError: No module named ‘_tkinter’

Solve:

pyenv uninstall 3.5.1
brew tap Homebrew/dupes
brew install tcl-tk
pyenv install 3.5.1

Reference:
http://qiita.com/hokkun-k/items/223b1125b814621a0c0e

向量 => 地理運算工具 => Buffer
向量 => 地理運算工具 => Intersect
向量 => 地理運算工具 => Dissolve

屬性表格 => 開啟欄位計算

From:  https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1

I didn’t do a PhD on machine learning (was mostly focused on Signal Processing and Software Engineering) so I get this question a lot. The typical person that asks me this question is a software engineer with a computer science background, so I will address it from that perspective. If you are a Math major, for example, my answer might be less useful.

The first thing I tell someone who wants to get into machine learning is to take Andrew Ng’s online course. I think Ng’s course is very much to-the-point and very well organized, so it is a great introduction for someone wanting to get into ML. I am surprised when people tell me the course is “too basic” or “too superficial”. If they tell me that I ask them to explain the difference between Logistic Regression and Linear Kernel SVMs, PCA vs. Matrix Factorization, regularization, or gradient descent. I have interviewed candidates who claimed years of ML experience that did not know the answer to these questions. They are all clearly explained in Ng’s course. There are other online courses you can take after this one such as

My recommended next step is the following. Get a good ML book (my list below), read the first intro chapters, and then jump to whatever chapter includes an algorithm you are interested. Once you have found that algo, dive into it, understand all the details, and, especially, implement it. In the previous online course you would already have implemented some algorithms in Octave. But, here I am talking about implementing an algorithm from scratch in a “real” programming language. You can still start with an easy one such as L2-regularized Logistic Regression, or k-means, but you should also push yourself to implement more interesting ones such as LDA (Latent Dirichlet Allocation) or SVMs. You can use a reference implementation in one of the many existing libraries to make sure you are getting comparable results, but ideally you don’t want to look at the code but actually force yourself to implement it directly from the mathematical formulation in the book.

So, what are some good books to do this? Many have been mentioned before. Some of my favorite:

• Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning
• Bishop’s Pattern Recognition and Machine Learning* David Barber’s Bayesian Reasoning and Machine Learning
• Kevin Murphy’s Machine learning: a Probabilistic PerspectiveYou can also go directly to a research paper that introduces an algorithm or approach you are interested on and dive into it.

My main point is that machine learning is both about breadth as depth. You are expected to know the basics of the most important algorithms (see my answer to What are the top 10 data mining or machine learning algorithms?). On the other hand, you are also expected to understand low-level complicated details of algorithms and their implementation details. I think the approach I am describing addresses both these dimensions and I have seen it work.

Edit 08/26/2015

In response to some comments and questions, I feel that I should add another book recommendation. If you feel like you lack some background in Statistics, I would totally recommend:

• Larry Wasserman’s All of Statistics: A Concise Course in Statistical InferenceThis books cover all the topics in statistics that you need for understanding ML concepts. As a matter of fact, the book itself has a pretty good introduction to many of the typical ML approaches such as regression and classification.

From:
https://dotblogs.com.tw/aken1215/2016/02/15/234033

NAS OS:
freenas => require 8G RAM
nas4free
openmediavault

I choose the openmediavault.

Notes:

• The drive that installed openmediavault cannot be the data drive.
• Install extra plugin from webUI ( http://omv-extras.org/simple/index.php?id=how-to-install-omv-extras-plugin ).
• In general setting, set the Session timeout to 0 to prevent logout.

http://stackoverflow.com/questions/7927750/specify-an-ssh-key-for-git-push-for-a-given-domain
https://gist.github.com/jexchan/2351996

List all containers:

docker ps -a`</pre>
Remover all stopped containers:
<pre style="background-color: #eeeeee; border: 0px; color: #222426; font-family: Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif; font-size: 13px; margin-bottom: 1em; max-height: 600px; overflow: auto; padding: 5px; width: auto; word-wrap: normal;">`docker rm `docker ps -aq`

docker-machine ls

To Prevent the virtualbox machine use many memory

First remember set up the config correctly.
Or you don’t mount config folder in the first time and copy the config by command:

docker cp <containerId>:/file/path/within/container /host/path/target

Run by this command:
docker run -d -p 9200:9200 -p 9300:9300 -v “$PWD/data”:/usr/share/elasticsearch/data -v “$PWD/config”:/usr/share/elasticsearch/config elasticsearch /bin/bash -c ‘usermod -u 1000 elasticsearch; gosu elasticsearch elasticsearch’

From:
https://hub.docker.com/_/elasticsearch/