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Deconstructing Deep Learning + δeviations

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Notes from 100 Page ML Book

I decided to add notes to this blog too. All such notes will be tagged with "book" for easier search. This one is my notes while reading "Andriy Burkov : The Hundred-Page Machine Learning Book". Amazon. Do support the author if you can.

A quick note on how I make notes. I first annotate the pdf of the book. And then type down the text to make it searchable. Yes I probably could use OCR but this helps me remember more. Also, this is not meant to be comprehensive reviews but only what I find interesting from the book. I read a lot about Deep Learning so these will keep popping up.

Okay now let us get to it :)

Initial thoughts from the content



Random variable

Unbiased estimator

Shallow learning

Cost func

Decision tree



Data imputation


Hyper param




Other learnings

Semi supervised

Zero shot

Combine models

Other stuff