new entry!

Author: drinkingkazu

content/2018-03-02-collab_tutorial.md

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+Title: Tutorials on Colaboratory
+Date: 2018-03-02 00:00
+Modified: 2018-03-02 00:00
+Category: tutorial
+Tags: python tutorial, DL tutorial, colaboratory, tensorflow
+Slug: 2018-03-02-Colaboratory-Tutorial-Summary
+Authors: Kazuhiro Terao
+Summary: List of tutorials covering basics of tensorflow, slim, image classification and semantic segmentation using MNIST images. All notebooks can be run on free Google colaboratory with GPU. No need to own your own GPU machine!
+
+This is a compilation of jupyter notebooks written for hands-on workshops held at Stanford/SLAC during last month (February/March 2018). They are targetted toward beginners, starting from how to use tensorflow and ends (currently) with training a deep convolutional neural network for semantic segmentation (pixel-level object categorization).
+
+All notebooks referenced here are executable on google colaboratory with a free K80 GPU. You do not need your own GPU machine or an account (well, you do need gmail account to use colaboratory).
+
+0. [Start google colaboratory](http://deeplearnphysics.org/Blog/colab-tutorials/2018-03-02-Colab-00.html)
+1. [Introduction to tensorflow: linear regression](http://deeplearnphysics.org/Blog/colab-tutorials/2018-03-02-Colab-01-TFIntro.html)
+2. [MNIST data set for ML-101](http://deeplearnphysics.org/Blog/colab-tutorials/2018-03-02-Colab-02-MNIST.html)
+3. [MNIST image classification](http://deeplearnphysics.org/Blog/colab-tutorials/2018-03-02-Colab-03-MNIST-Classification.html)
+4. [Tensorflow-Slim: high level APIs for building a graph](http://deeplearnphysics.org/Blog/colab-tutorials/2018-03-02-Colab-04-MNIST-Classification-Slim.html)
+5. [Semantic segmentation with shallow CNN](http://deeplearnphysics.org/Blog/colab-tutorials/2018-03-02-Colab-05-MNIST-Segmentation-Shallow.html)
+6. [Semantic segmentation with deep CNN](http://deeplearnphysics.org/Blog/colab-tutorials/2018-03-02-Colab-06-MNIST-Segmentation-Deep.html)
+
+Your feedbacks are always welcome! You know better how to improve them so that we can guide others better. Suggestions to include more items (like other network tasks) are also welcome! We will try our best to keep this list interesting to new members.
+