- install conda: https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html
- clone the repositority
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create a conda environment with required packages (in console):
conda create -n ml_workshop scikit-learn matplotlib ipykernel
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Activate conda environment and start the Jupyter notebook:
conda activate ml_workshop python -m ipykernel install --user --name ml_workshop --display-name "ML Workshop" jupyter notebook
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Alternative: run in VSCode with Python and Jupyter notebook extensions
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if you don't want to set up a local version, you can try this browser-based notebook: https://jupyter.org/try-jupyter/retro/notebooks/?path=notebooks/Intro.ipynb
Dataset is available on Kaggle - download and extract the train folder into your working directory.
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create a conda environment with required packages (in console):
conda create -n ml_workshop2 tensorflow-gpu
This requires your GPU to be CUDA-enabled - you may have to install additional drivers depending on your hardware. -
alternative: train using CPU (much slower):
conda create -n ml_workshop2 tensorflow
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Install remaining libraries:
conda activate ml_workshop2 conda install keras scikit-learn ipykernel pillow h5py python -m ipykernel install --user --name ml_workshop2 --display-name "ML Workshop - cats vs. dogs" jupyter notebook