Success in Machine Learning starts with Mathematics
I recently came across a great resource => Mathematical Foundations of Machine Learning.
It highlights a critical perspective - Machine Learning algorithms aren't just for performing inference, they are algorithms for building inference algorithms from examples.
3 Takeaways :
1./ Occam’s Razor
In a world of complex models, the simplest solution that explains the data is often the best.
2./ The Power of SVD
How Singular Value Decomposition can compress a complex image into a fraction of its size without losing its essence.
3./ Interpretability:
Why Decision Trees remain attractive models when we care about why a decision was made.
If you're looking to strengthen your AI toolkit, start with the math!
If you need the pdf, just let me know :)
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