Link to Content:
Neural Networks from Scratch (in R)
Content Found Via:
Data Science Renee
Tags: machine learning / neural networks / R
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:
- “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).
- 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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