Hands-on: Deep Learning and CNN

Slides and Source Code

Originally shared by Stanford CS231n using MIT License. I modified them to be PEP8-compliant and Py3k-compatible and also make them shorter to fit in a one-hour talk.

Setup the Envorinment

Root Python version

Recommend to use miniconda3 or Anaconda3 (they are same in essence). This should work on Windows, Linux, and OSX.

I target for Python 3.4+ but should be fine on Pyhton 3.3. Never ask me how to run on Python 2.7. ...okay, the original source runs on Python 2.7 and I simply change some library import path and classic 2vs3 difference.

(Skippable) Setup using pyenv

You can use pyenv to manage multiple Python versions without breaking the system-wide setting. Follow pyenv's readme to set up, which has been tested to work on Debian, Ubuntu, and OSX.

# under this repo root
pyenv install miniconda3-3.8.3
pyenv local miniconda3-3.8.3

Now all python command under this repo root use miniconda's python, which can be checked by

pyenv which python
# ~/.pyenv/versions/miniconda3-3.8.3/bin/python
python
# Python 3.4.3 |Continuum Analytics, Inc.| (default, Mar  6 2015, 12:07:41)
# ...
# >>>

Python virtual environment

(Mini)conda handles the virtual environment itself. It is powerful and makes everythin simple for numerical computing packages.

conda create -n dnn python=3.4 \
	numpy cython            \
	matplotlib              \
	ipython-notebook

Activate and deactivate the envrionment is easy,

source activate dnn-mkl  # activate
deactivate               # deactivate

Init the dataset

(TODO)

授權 License

The slide is powered by

• reveal.js: HTML5 framework by Hakim El Hattab et al., under MIT license
• highlight.js: Syntax highlight library by Ivan Sagalaev et al., under MIT license

除另外標示，本

• 投影片內容（slides目錄下）係使用創用 CC 姓名標示 4.0 國際（Creative Commons 4.0 BY International）授權條款授權。

• 程式碼係使用 MIT 授權。

授權條款可以分別參見檔案 CC 4.0 使用條款以及LICENSE_MIT。