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This repository contains a short introduction into the basic workflow of TUM.ai projects. It quickly introduces git, python3, virtual environments and jupyter notebooks.

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TUM.ai Onboarding - Tech Setup

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Hello and welcome at TUM.ai. This short tutorial gives an introduction into the technologies that will be used often at TUM.ai. If you have already worked on software projects, you might already know most of these technologies. Then feel free to skip the tutorial or parts of it.

This tutorial is made for Linux and Windows, the Mac installation should be very similar to the Linux one.

The technologies included in this tutorial are:

  1. Git
  2. Python3 and PyCharm
  3. Python3 - venvs
  4. Python3 - Important packages overview
  5. Python3 - Jupyter Notebook

Throughout this tutorial, you will see some TASKs. These tasks should be completed for this tutorial.
If there are any questions, please use our Slack channel or the Whatsapp Group.

The first tutorials are about setting up the basic workflow an learning how to do software development in a group of people.

The last tutorial goes one step further. This tutorial will showcase a standard procedure of creating and training AI models. It is meant to allow you to get your hands dirty and start experimenting with AI.
We will showcase that for the three main Machine Learning Frameworks - for now we have covered the first two.

This tutorial will not cover the mathematical foundations of AI, but one important thing to understand is what these libraries actually do. Their main purpose, obviously, is to allow developers, researches to create, train and test AI models.
An important feature is the so-called "Autograd" functionality. That means the main power of these frameworks is that they automate a very big part of the training process - namely the computation of gradients. As you'll learn or maybe already know, gradients are at the core of Machine Learning - they need to be calculated to actually do the learning (i.e. change the model parameters based on an error). All three programs provide different ways of using this functionality, but ultimately they are so powerful because they do the complete calculation of the gradients for you.

Don't panic if this information doesn't tell you anything, do the tutorials, read some information of our tutorial list and keep on coming back to this tutorial section. Alternatively, just ask in the #ai_beginners channel.

You can jump to the tutorials here:
git:
Git Linux | Git Windows

python:
Python Linux | Python Windows

venv:
Venv Linux | Conda Env Windows

packages:
Package Linux | Packages Windows

jupyter notebook:
Jupyter Linux | Jupyter Windows

This tutorial was written by Jakob Kruse, Founder at TUM.ai.
If you notice some errors or bugs, please report them to contact@tum-ai.com .

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This repository contains a short introduction into the basic workflow of TUM.ai projects. It quickly introduces git, python3, virtual environments and jupyter notebooks.

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