Introduction to Deep Learning
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Depending on time, some of the topics may not be covered during the workshop. But, please note that the entire content(data and source code in
ipython notebookformat) would be available in this repository.
Pre-requisites: git, python 2.7.X, virtualenv, pip (7.1.X recommended)
$ sudo apt-get install python2.7 python-dev build-essential curl libatlas-base-dev gfortran $ sudo apt-get install libfreetype6-dev libpng-dev libjpeg-dev
Clone the repo from GitHub
$ git clone https://github.com/rouseguy/intro2deeplearning.git $ cd intro2deeplearning
Create python virtual environment
$ virtualenv env $ source env/bin/activate
Install requirements using pip
$ pip install -r requirements.txt
requirements.txtif you're on linux
When the requirements are being downloaded / installed, Fetch the datasets simultaneously
$ sh download_data.sh
Run check_env.py script to test the dependencies
$ python check_env.py
Output should look like this
[ OK ] scipy version 0.15.1 [ OK ] PIL version 1.1.7 [ OK ] keras [ OK ] IPython version 4.0.0 [ OK ] theano version 0.7.0 [ OK ] numpy version 1.9.2 [ OK ] pandas version 0.16.2 [ OK ] gensim version 0.10.3 [ OK ] sklearn version 0.16.1
This means you have all the dependencies installed and you're ready to start.
Run the notebook
$ cd notebooks $ ipython notebook
This opens your default browser which displays the list of notebooks in the current directory.
Open 1. Introduction to Artificial Neural Networks.ipynb. Now, run the first cell with imports in the notebook (shift + enter). If you have all the dependencies installed, this should run without any errors.
Note: We only support Ubuntu Linux (Tested) & OSX environments. We strongly recommend Windows users to have a VM running Linux, and then install these requirements on that VM.