Add tutorial notebooks covering installation to evaluation

[Sharkyii]

Describe your changes

Added 5 tutorial notebooks to help new users get started with Neural-LAM:

1. Getting started guide - installation through first prediction
2. Working with custom datasets
3. Understanding and optimizing graph structures
4. Advanced training techniques
5. Evaluating and visualizing results(This is on the way 🚧)

Issue Link

For GSoC Project-2

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

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• My branch is up-to-date with the target branch - if not update your fork with the changes from the target branch (use pull with --rebase option if possible).
• I have performed a self-review of my code
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Each PR comes with its own improvements and flaws. The reviewer should check the following:

• the code is readable
• the code is well tested
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Checklist for assignee

• PR is up to date with the base branch
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[sadamov]

@Sharkyii thanks a lot for these four notebooks. The getting_started one has a lof of overlap with #202. Would you mind implementing your ideas in that PR directly for the first notebook? Especially your visuaizations seem pretty nice!

Just wanted to mention this to prevent duplicate work.

[Sharkyii]

@sadamov Sure, i am happy to do that.

[sadamov]

I'll mark this as a draft for now since it is rather related to a GSoC project proposal and not directly an issue here on GitHub. As mentioned before there is great work going on in #202 where we can continue the discussion.