Physicists have an unfair advantage getting into machine learning ⚛

All applied scientists have a leg up for working with data, but physicists have to deal with noisy data, math, and programming daily.

Here's a grab bag of advantages and shortcuts into ML

## ↪️ Inversion work

When you squint, computational inversion looks just like machine learning.

Here are the concepts used in both:

- optimization
- regularization
- loss functions & metrics

The big difference is the model we optimize.

## 🔢 Years and years of math

Yes, you can do applied machine learning with minimal math.

But the mathematical intuition physicists gain from university and then working around math for years helps make ML infinitely easier.

Grokking new concepts and models becomes trivial.

## 💾 Data sucks

Physicists work with data that is noisy and incomplete.

In data science and machine learning, it's a huge advantage having experience with different types of data.

Figuring out what is noise and what is signal is strong inductive bias in modeling data systems.

## 💻 Programming new new things

Computer science is nice and a DSA course has really helped me.

In physics you have to figure out so many things from scratch. Program efficiently and somehow just make it work. And it has to be fast!

Thrifty coding is an advantage in ML and DS

## Conclusion

Physicists have an unfair advantage venturing into ML using:

- Thrifty programming knowledge
- Intuitive math understanding
- Work with optimization

And of course the ability to keep learning hard things!