This tutorial is split into two practical parts.

1. ML_intro provides an introduction to classical Machine Learning approaches with sklearn.

2. DL_tutorial introduces convolutional neural networks (CNNs) with keras and tensorflow.

Clone this repository to your local machine with:

git clone https://github.com/langnico/MLEG_tutorial.git

Download the required data for the "DL_tutorial" from this link:

https://drive.google.com/open?id=1KoR9ISddhHsecsZG0lmePNONYKOZns1E

Move the directories into the DL_tutorial/ directory. The directory tree should look like this:

• DL_tutorial/
  • data/
  • model_weights/
  • pretrained_models_imageNet/

We are going to write and execute the code in a jupyter notebook. The DL_tutorial will use keras with a tensorflow backend.

Therefore, we need to install:

• python3
• jupyter
• tensorflow

Further we will need the python packages/modules:

• sklearn
• numpy
• matplotlib
• pandas
• keras

We propose to install python via anaconda.

1. Install Anaconda and read the Anaconda tutorial (20min)

2. Create a new environment: conda create --name MLEGenv python=3.6

3. Activate the new environment

   • Windows: activate MLEGenv
   • Linux and macOS: source activate MLEGenv

   --> now your terminal prompt should start with (MLEGenv)

4. Install the following packages in your activated MLEGenv:

   conda install jupyter
   conda install scikit-learn
   conda install pandas
   conda install matplotlib
   conda install keras
   

5. Install tensorflow following the: official installation instructions

1. In the activated MLEGenv type which jupyter. This should point to the python installation in your conda env e.g. /username/anaconda3/envs/DL_tutorial/bin/jupyter

2. Open a terminal and go to the location of the file: installation_check.ipynb

   Then open the jupyter notebook with: jupyter notebook installation_check.ipynb

   NOTE: If this does not automatically open a browser showing the notebook, then open a browser (Firefox, Chrome) and type: http://localhost:8888/notebooks/installation_check.ipynb

   Then select the first cell containing the imports and click on the > Run Button. If your installation was successful, the output should be like this:

   Using TensorFlow backend.
   successfully imported
   keras version:  2.2.4
   

• https://github.com/tgjeon/Keras-Tutorials
• https://github.com/flyyufelix/cnn_finetune

• Riccardo De Lutio
• Mikhail Usvyatsov
• Nico Lang