DL_1819

Deep Learning Course, University of Twente

DL_1819: Deep Learning - From Theory to Practice
Christoph Brune

Python notebooks of the course

Jypyter Online Repository - Binder

3 Tutorial: Introduction to Python, MLP and Backpropagation

9 Tutorial: CNN and RNN

12 Tutorial: VAEs and GANs

Local Python installation

Follow the following instructions to install Miniconda and create a Python environment for the course:

1. Download the Python 3.6 installer for Windows, macOS, or Linux from https://conda.io/miniconda.html and install with default settings. Note for Windows: If you don't know if your operating system is 32-bit or 64-bit, then open Settings-System-About-System type to find out your xx-bit system.

   • Windows: Double-click on the Miniconda3-latest-MacOSX-x86_64.exe file.
   • macOS: Run bash Miniconda3-latest-MacOSX-x86_64.sh in your terminal.
   • Linux: Run bash Miniconda3-latest-Linux-x86_64.sh in your terminal.

2. Windows: Open the Anaconda Prompt terminal from the Start menu. MacOS, Linux: Open a terminal.

3. Install git: conda install git.

4. Download the GitHub repository of the course: git clone https://github.com/ChristophBrune/DL_1819.

5. Go to folder DL_1819 with cd DL_1819, and create a Python virtual environment with the packages required for the course: conda env create -f environment.yml. Note that the environment installation may take some time.

   Notes:
   The installed conda packages can be listed with conda list.
   Some useful Conda commands are pwd, cd, ls -al, rm -r -f folder/
   Add a python library to the Python environment: conda install -n DL_1819 numpy (for example)
   Read Conda command lines for packages and environments
   Read managing Conda environments

Running local Python notebooks

1. Windows: Open the Anaconda Prompt terminal from the Start menu. MacOS, Linux: Open a terminal.

2. Activate the environment. Windows: activate deeplearn_course, macOS, Linux: source activate deeplearn_course.

3. Download the python notebooks by direct downloads from the next section or with GitHub with the command git pull.

4. Start Jupyter with jupyter notebook. The command opens a new tab in your web browser.

5. Go to the exercise folder, for example DL_1819/codes/1_tutorial/lab01_python.

   Notes:
   Windows: Folder DL_1819 is located at C:\Users\user_name\DL_1819. MacOS, Linux: /Users/user_name/DL_1819.