The best way to get experience and knowledge in machine learning is to build things.
If you build things, you should be compensated in proportion to the value you create.
In practice, this is often not the case. Not in open source, not in the industry.
What can change this?
Well, DAO-s have this potential. Of course, the concept may not live up to the expectations, but their fundamental idea holds a lot of promise.
In a DAO, contributions are rewarded with tokens. Decisions are made by voting, where all token-holders can participate.
Tokens also provide a way for stakeholders to receive the profits from the products, proportional to their work.
This system incentivizes quality contribution.
You create value. You receive value.
Of course, we still have a long way to go. As the project is very new, the governance is not yet set up. This is a task for the upcoming weeks.
We are just getting started.
If you are interested in pushing the boundaries of machine learning and software, I recommend you to read up on DAO's, educate yourself on the (real) promise of web3, and check out the community.
We'll be waiting for you if you are up to the challenge.
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A concise guide from zero to one. 100% knowledge, 0% fluff. 🠓
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Functions, the central objects of mathematics and computer science, are just mappings of inputs to outputs.
A convenient (albeit quite imprecise) way to define them is to describe their effect. An explicit formula is often available, which we can translate to code.
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However, giving an explicit formula is not always easy or possible.
For instance, can you calculate the number of ways we can order a deck of n cards by shuffling its cards?
The single best way to get into machine learning is to build something with it.
Here is an extensive list of hands-on projects that you can start right now. Take inspiration, learn tools, and find the topics you are passionate about.
Read on and go create something awesome. ↓
I am grouping the projects into the following categories.
These hands-on projects work the best when you
• follow along and do the coding as well,
• understand why and how things work,
• and try to bring what you built to the next level.
The reason PhD school is difficult is not because of the research.
Besides that, there are several key choices whose importance is underestimated by the students. Most of them are unrelated to your hard skills.
Here are the most impactful ones. ↓
1. Picking your advisor.
Young researchers usually value fame and prestige over personal relations. However, your advisor and your fellow labmates will determine your everyday work environment.
Don't sacrifice this for some scientific pedigree.
A healthy relationship with your advisor is essential for your professional performance. Pick someone who is not only a good scientist but a good person as well. Avoid abusive personalities.
Interview students and lab alumni about your prospective advisor if you can.