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README.md

Coursework materials

Completing in a cloud without installation

Binder

This is as simple as clicking on the binder badge above and requires to be connected to internet. Although, this option comes at the cost of lower computational resources being available to you, but it will be sufficient to successfully complete all assignments.

Note: It may take a couple of minutes to launch a binder server. If it takes longer then that, try to refresh the web page before reporting this issue.

Completing on your personal computer

  1. Install Anaconda - installation file (use python 3.7).

  2. Install JupyterLab in the base environment - instructions. Normally, it comes with Anaconda installation by default.

  3. Get source files.

    Preferred option is to clone this repository using git.

    git clone https://github.com/IlyaKisil/dpm-coursework.git

    If this is the first time you hear about git, it is recommended to watch one of many introductory videos about it, for example on YouTube.

    Alternatively, you can download a ZIP folder with all materials for this assignment by using the Clone or Download button (in green color) at the top of this page.

  4. Bootstrap virtual environment.

    If you are on Unix, then execute in terminal:

    cd dpm-coursework
    
    ./boostrap-venv.sh

    If you are on Windows, then open Anaconda prompt:

    cd dpm-coursework
    conda create -y --name "dpm-coursework" python=3.6.5
    conda activate "dpm-coursework"
    pip install binder\coursework
    python -m ipykernel install --user --name "dpm-coursework" --display-name "dpm-coursework"    
  5. Start JupyterLab and open a notebook with table of contents (should look like this). You can find it under the notebooks directory.

Reporting problems and issues

Please use one of these forms which supports markdown text formatting. It would also be helpful if you include as much relevant information as possible. This could include screenshots, code snippets etc.

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📙 Materials for EE4-13 Adaptive Signal Processing and Machine Intelligence (2019-2020)

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