CE7454: Deep Learning for Data Science
Semester 1 2018/19
Xavier Bresson

Running Python notebooks without local Python installation

    Run the notebooks from the cloud using Binder: Simply click here.

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/xbresson/CE7454_2018.

5. Go to folder CE7454_2018 with cd CE7454_2018, 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 CE7454_2018 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 CE7454_2018/codes/lab01_python.

   Notes:
   Windows: Folder CE7454_2018 is located at C:\Users\user_name\CE7454_2018. MacOS, Linux: /Users/user_name/CE7454_2018.
   

Python notebooks of the course

Note: The datasets are too large for GitHub. They will be automatically downloaded when running the codes, or you can directly download the datasets from CE7454_2018/codes/data or the zip file Download datasets.

Download datasets

Labs lecture 03: Python and PyTorch

Labs lecture 04: Vanilla Neural Networks Part 1

Labs lecture 06: Vanilla Neural Networks Part 2

Labs lecture 07: Multi-Layer Perceptron Part 1

Labs lecture 08: Multi-Layer Perceptron Part 2

Labs lecture 10: Convolutional Neural Networks

Labs lecture 13: Recurrent Neural Networks

Labs lecture 17: Introduction to Graph Science

Labs lecture 18: Graph Neural Networks Part 1

Labs lecture 19: Graph Neural Networks Part 2

Labs lecture 20: Deep Reinforcement Learning