Neural Differential Equations

This won the best paper award at NeurIPS (the biggest AI conference of the year) out of over 4800 other research papers! Neural Ordinary Differential Equations is the official name of the paper and in it the authors introduce a new type of neural network. This new network doesn’t have any layers! Its framed as a differential equation, which allows us to use differential equation solvers on it to approximate the underlying function of time series data. Its very cool and will ultimately allow us to learn from irregular time series datasets more efficiently, which applies to many different industries. I’ll cover all the prerequisites in this video and point to helpful resources down below. Enjoy!

Code for this video:
https://github.com/llSourcell/Neural_Differential_Equations/

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More learning resources:
https://www.youtube.com/watch?v=q555kfIFUCM&lc=z23fvvbypqe3jzrc2acdp432igdvd11cmfijivtyfvxw03c010c
https://www.youtube.com/watch?v=h3l4qz76JhQ&t=9s
https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
https://towardsdatascience.com/paper-summary-neural-ordinary-differential-equations-37c4e52df128
https://arxiv.org/abs/1806.07366
https://blog.acolyer.org/2019/01/09/neural-ordinary-differential-equations/
https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/

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