Deep Learning for Symbolic Mathematics!? | Paper EXPLAINED

"Neural Nets are inexact beasts that will never solve exact problems", right? Wrong. Ms. Coffee Bean explains, draws and animates how neural networks can solve symbolic mathematics problems, e.g. integration, ODEs. It can even tackle integrals that Mathematica fails to solve. Do not worry, Mathematica, you are still awesome!
Amazing work by Guillaume Lample and François Charton @AIatMeta .
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📄 Lample, Guillaume, and François Charton. "Deep learning for symbolic mathematics." arXiv preprint arXiv:1912.01412 (2019). https://arxiv.org/pdf/1912.01412.pdf

📺 Ms. Coffee Bean explains the Transformer: https://youtu.be/FWFA4DGuzSc

Outline:
* 00:00 Neural networks integrate and solve ODEs. So what?
* 03:55 Generating training data
* 06:42 Representing and generating random functions
* 07:56 Symbolic equations with neural nets

Music 🎵 : Pretty Boy by DJ Freedem

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#paper_explained #symbolic_mathematics #differentiation #integration #Mathematica #integration_by_parts #Guillaume_Lample #Francois_Charton #transformers_in_mathematics #ODEs #ordinary_differential_equations #self-supervised_learning #NLP #AI #artificial_intelligence #machine_learning #visualized #deep_learning #easy #beginner #explained #computer_science #women_in_ai #animated #letitia_parcalabescu #Fast_Forward_Science #competition #Science #Knowledge #Research #Wissenschaft_im_Dialog

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