Merge branch 'develop' into tmw_pytorch_dev

Author: twongjirad

.travis.yml

@@ -0,0 +1,22 @@
+language: python
+branches:
+  only:
+    - develop
+install:
+- pip install pelican
+- pip install ghp-import
+- pip install markdown
+- pip install jupyter notebook
+- pip install ipython
+- pip install nbconvert
+script:
+- make html
+notifications:
+# Emails are sent to the committer's git-configured email address by default,
+# but only if they have access to the repository.  To enable Travis on your
+# public fork of Caffe, just go to travis-ci.org and flip the switch on for
+# your Caffe fork.  To configure your git email address, use:
+#     git config --global user.email me@example.com
+  email:
+    on_success: always
+    on_failure: always

README.md

@@ -1,3 +1,5 @@
+[![Build Status](https://travis-ci.org/DeepLearnPhysics/Blog.svg?branch=develop)](https://travis-ci.org/DeepLearnPhysics/Blog)
+
 # Blog
 A repository for the DeepLearnPhysics group [Blog](https://deeplearnphysics.org/Blog) webpage.
 The `master` branch holds static HTML files generated by [Pelican](http://docs.getpelican.com/en/stable/) with the [flex theme](https://github.com/alexandrevicenzi/Flex). We use the [pelican-ipynb](https://github.com/danielfrg/pelican-ipynb) plugin to easily turn a juypyter notebook into a blog.

content/2017-12-29-BrowsingClassificationData_v0.1.0.ipynb

@@ -6,12 +6,14 @@
    "source": [
     "Our first [public data set](http://deeplearnphysics.org/DataChallenge/#ChallengeClassification) is available! We are very excited for this 1st step to have public data set with [training tutorials](http://deeplearnphysics.org/Blog/tutorial_summary.html). This notebook is an announcement + browsing of the file contents. This may be a useful guide before jumping into the tutorial. Since this notebook is a bit lengthy, here's an outline of what's covered.\n",
     "\n",
-    "1. [Overall summary of sample generation configuration](#overview)\n",
-    "2. [Exploring file contents](#file_contents)\n",
-    "3. [Example image dump](#image_dump) (data analysis)\n",
-    "4. [Particle-wise event fraction](#event_fraction) (data analysis)\n",
-    "5. [Energy and momentum distribution](#kinematics) (data analysis)\n",
-    "6. [Image filtering effect](#filter_effect) (data analysis)"
+    "0. [Overall summary of sample generation configuration](#overview)\n",
+    "1. [Exploring file contents](#file_contents)\n",
+    "2. [Example image dump](#image_dump) (data analysis)\n",
+    "3. [Particle-wise event fraction](#event_fraction) (data analysis)\n",
+    "4. [Energy and momentum distribution](#kinematics) (data analysis)\n",
+    "5. [Image filtering effect](#filter_effect) (data analysis)\n",
+    "\n",
+    "Before anything, make sure you have necessary software stacks (`larcv`, python packages) and can execute these imports."
    ]
   },
   {

content/2018-01-01-BrowsingSegmentationData_v0.1.0.ipynb

@@ -0,0 +1,10 @@
+Title: Browsing Semantic-Segmentation Data Set (v0.1.0)
+Slug: 2018-01-01-BrowsingSegmentationData_v0.1.0
+Lang: en
+Date: 2018-01-01
+Category: public data
+Tags: public data, analysis example
+Authors: Kazuhiro Terao
+Summary: Browse through the file contents of the first semantic-segmentation data release (v0.1.0). I go over an introduction to _semantic-segmentation_ image analysis task, sample generation configurations, and further cover information that can be used for training algorithms for object detection and even instance-wise semantic segmentation. I also show some physics of the included physics events.
+
+