Material Advanced Topics in Machine Learning 2021/22

Want to run this project using a Docker image?

Read the Docker instructions.

Want to install this project on your own machine?

Start by installing Anaconda (or Miniconda), git, and if you have a TensorFlow-compatible GPU, install the GPU driver.

Next, clone this project by opening a terminal and typing the following commands (do not type the first $ signs on each line, they just indicate that these are terminal commands):

$ git clone [email protected]:atml202122/code.git
$ cd code

If you want to use a GPU, then edit environment.yml (or environment-windows.yml on Windows) and replace tensorflow=2.0.0 with tensorflow-gpu=2.0.0. Also replace tensorflow-serving-api==2.0.0 with tensorflow-serving-api-gpu==2.0.0.

Next, run the following commands:

$ conda env create -f environment.yml # or environment-windows.yml on Windows
$ conda activate atml_202122
$ python -m ipykernel install --user --name=python3

Then if you're on Windows, run the following command:

$ pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py

Finally, start Jupyter:

$ jupyter notebook

If you need further instructions, read the detailed installation instructions.

Acknowledgements

This repository is based on and extends the accompanying repository of the book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurelien Geron, 2nd Edition, O'Reilly 2019. Exercises and labs are based on and in some cases extends those from the Deeplearning Specialisation.