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Neural Networks from Scratch (in R)

Created/Published/Taught by:
Ilia Karmanov

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Data Science Renee

Free? Yes

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From Medium:

This tutorial: “is for those of you with a statistics/econometrics background but not necessarily a machine-learning one and for those of you who want some guidance in building a neural-network from scratch in R to better understand how everything fits (and how i doesn’t).”

The author’s motivations for writing the tutorial are:

  1. “Understanding (by writing from scratch) the leaky abstractions behind neural-networks dramatically shifted my focus to elements whose importance I originally overlooked. If my model is not learning I have a better idea of what to address rather than blindly wasting time switching optimisers (or even frameworks).
  2. A deep-neural-network (DNN), once take apart into lego blocks, is no longer a black-box that is inaccessible to other disciplines outside of AI. It’s a combination of many topics that are very familiar to most people with a basic knowledge of statistics. I believe they need to cover very little (just the glue that holds the blocks together) to get an insight into a whole new realm.

Starting from linear regression we will work through the maths and the code all the way to a deep-neural-network (DNN) in the accompanying R-notebooks. Hopefully to show that very little is actually new information.”

Recommended Prerequisites: this post is designed for people "with a statistics/econometrics background but not necessarily a machine-learning one"

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