ቆይ አንዳንድ ሠዎች ማርፈድ በደማቸው ነውንዴ ያለው like fr እህህህህ specially demo የኛዎቹ literally everyone i know is አርፋጅ weys their clock runs on a different timezone than the rest of us? ወገን atleast Let’s try to sync up የምር ያስተዛዝባል😭
😁3
Robi makes stuff
Bout to take another picture , say cheeseeeeee 📸
From messy datasets to smart models—learning never stops!
#TGYearBook25
#TGYearBook25
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Machine Learning from Scratch by Danny Friedman
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
🌟 Link: https://dafriedman97.github.io/mlbook/content/introduction.html
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
🌟 Link: https://dafriedman97.github.io/mlbook/content/introduction.html
Forwarded from Oops, My Brain Did That (Mike)
My personality basically was shaped by Monica’s ocd, Chandler’s sarcasm and Ross’s awkwardness
🚀 Ever wondered why your machine learning model struggles—even when your data looks clean?
It might be because your features are *speaking different languages*
Some values are in thousands, others in decimals—and your model? It's just trying to make sense of the chaos.
That's where feature scaling becomes a game-changer. Let's break it down. 👇
It might be because your features are *speaking different languages*
Some values are in thousands, others in decimals—and your model? It's just trying to make sense of the chaos.
That's where feature scaling becomes a game-changer. Let's break it down. 👇
🎯 What is Feature Scaling in Machine Learning?
Imagine you're training a model and one feature is in kilometers (like 1000s) while another is in centimeters (like 1s). Models like gradient descent or KNN will get confused, thinking big numbers matter more.
That’s where feature scaling steps in—bringing all features to the same level.
Imagine you're training a model and one feature is in kilometers (like 1000s) while another is in centimeters (like 1s). Models like gradient descent or KNN will get confused, thinking big numbers matter more.
That’s where feature scaling steps in—bringing all features to the same level.
There are two common ways to get all attributes to have the same scale: min-max
scaling and standardization.
Min-max scaling (many people call this normalization) is quite simple: values are
shifted and rescaled so that they end up ranging from 0 to 1.
so simply we do this by subtract‐
ing the min value and dividing by the max minus the min.
scaling and standardization.
Min-max scaling (many people call this normalization) is quite simple: values are
shifted and rescaled so that they end up ranging from 0 to 1.
so simply we do this by subtract‐
ing the min value and dividing by the max minus the min.
Standardization is quite different: first it subtracts the mean value (so standardized
values always have a zero mean), and then it divides by the standard deviation so that
the resulting distribution has unit variance. Unlike min-max scaling, standardization
does not bound values to a specific range, which may be a problem for some algo‐
rithms (e.g., neural networks often expect an input value ranging from 0 to 1).
values always have a zero mean), and then it divides by the standard deviation so that
the resulting distribution has unit variance. Unlike min-max scaling, standardization
does not bound values to a specific range, which may be a problem for some algo‐
rithms (e.g., neural networks often expect an input value ranging from 0 to 1).
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Just opened instagram after a while and this the first reel that showed up and jeez I really needed this
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