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31 lines
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31 lines
2.1 KiB
Markdown
# Machine Learning Exercises
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This repository contains the Python programming exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/). They are part of the curriculum of the [ML for Data Scientists](https://franziskahorn.de/mlws_scientist.html) and [ML in Practice](https://franziskahorn.de/mlws_practice.html) Workshops.
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If you have any questions, please send me an [email](mailto:hey@franziskahorn.de).
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Have fun!
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### Using Python
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The programming exercises are written in Python. If you're unfamiliar with Python, you should work through [this tutorial](https://github.com/cod3licious/python_tutorial) first.
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#### Working on your own computer
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The [Python tutorial](https://github.com/cod3licious/python_tutorial) includes some notes on how to install Python and Jupyter Notebook on your own computer. <br>
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Please make sure you're using Python 3 and all libraries listed in the [`requirements.txt`](/requirements.txt) file are installed and up to date. You can verify this with the [`test_installation.ipynb`](/test_installation.ipynb) notebook.
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#### Working in the cloud
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##### Using Google Colab
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If you have a Google account, you can also run the code in the cloud using Google Colab:
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[](https://colab.research.google.com/github/cod3licious/ml_exercises) <br>
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While Google Colab already includes most packages that we need, should you require an additional library (e.g., `skorch` for training PyTorch neural networks in notebook 6), you can install a package by executing `!pip install package` in a notebook cell. With Colab, it is also possible to run code on a GPU, but this has to be manually selected.
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##### Using MyBinder
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If you don't have a Google account, you can also use MyBinder, which does not require you to log in:
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[](https://mybinder.org/v2/gh/cod3licious/ml_exercises/main) <br>
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However, this will take a while to load and might be very slow or even crash due to insufficient RAM for some of the exercises.
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