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title: A Rainforest in Machine Learning author: Arjoonn Sharma @ CLAP Research patat: incrementalLists: true ...


When all you have is a hammer, everything is a nail. That's why we fevicol expands your carpentry.


So talk is all about decision trees?

No ๐Ÿคท

  • We discuss everything BUT neural networks.
  • Lots of good talks about neural networks, 3Blue1Brown for example.
  • Running neural networks in production is hard. Quora for example uses a version of decision trees.
  • Everybody needs โค.

Rebellion & Art ๐ŸŽจ


What was overthrown with the current wave of neural networks?

Unheard of ML ๐Ÿ˜ฎ

  • Now an empirically dominated field, machine learning is. -Yoda


There were supposed to be decision trees in this talk. Where are they?

Plato's Cave


How do you solve the bombing problem?

๐Ÿ’ฃ ๐Ÿ’ฃ ๐Ÿ’ฃ ๐Ÿ’ฃ


Decision trees are certainly diverse. So what?

Taller, stronger, sharper

  • Substitute 'conditions' by massive use of a simpler kind of compute "+-".
  • With decision trees we directly compute "conditions".
  • Better software provides direct benefit to usage.


I think I've seen some of these concepts before...

All may do what by man has been done

The old The new
Decision Trees MLP
CART Backprop
Bagging, Boosting Dropout, Distillation
Rotation Forest Convolution
Cascade Forest Deep Neural Networks
Directed Acyclic Graph Residual Connections
Meta GS Auto ML
Adversarial GS GAN


No talk is complete without questions. Please fire away! This talk can be found online

Parthian shot

CLAP Research Telegram Github

More references.

Conditional Random Fields Supervised forest kernel Born again trees Catboost Core Vector Machine Manifold learning Gini coefficient World model nn

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