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  • Mathematical Foundations of Machine Learning
  • The Slope of Everything: Gradient Descent from Scratch
  • LA30: Left , Right and Pseudo Inverse

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iyui.medium.com /1 month ago

Mathematical Foundations of Machine Learning

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medium.com /1 week ago

The Slope of Everything: Gradient Descent from Scratch

Math for Machine Learning: Series 2, Article 1Continue reading on Medium »

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medium.com /1 month ago

LA30: Left , Right and Pseudo Inverse

Find the whole Linear algebra series hereContinue reading on Medium »

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medium.com /1 month ago

Relearning Machine Learning: Fundamentals in Plain English

Before learning more, I think it’s worth understanding better.Continue reading on Medium »

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irshathazeem.medium.com /1 month ago

Linear Regression - ML Algorithm

IntroductionContinue reading on Medium »

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bionewsdigest.medium.com /4 weeks ago

A note on distance metrics in machine learning

I have found that to quickly understand a machine learning model is to first look at the objective / loss function. It directly tells you…Continue reading on Medium »

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medium.com /3 weeks ago

Singular Values and Determinants, Explained by a Pile of Unit Masses

Why centering is the whole game, and why the same matrix keeps showing up under different namesContinue reading on Medium »

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medium.com /2 weeks ago

Day 4 of My Data Science Journey -Understanding Matrix Multiplication, Transformations, and Dot…

Today’s learning session was one of the most interesting so far.Continue reading on Medium »

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towardsdatascience.com /1 month ago

The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition

The geometric foundations you need to understand the dot product The post The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition appeared first on Towards Da...

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towardsdatascience.com /1 month ago

Linear Regression Is Actually a Projection Problem (Part 2: From Projections to Predictions)

The Vector View of Least Squares. The post Linear Regression Is Actually a Projection Problem (Part 2: From Projections to Predictions) appeared first on Towards Data Science.

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johndcook.com /2 weeks ago

The linear algebra of bit twiddling

The previous post looked at the tempering step of the Mersenne Twister, formulating a sequence of bit operations as multiplication by a matrix mod 2. This post will look at the com...

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johndcook.com /1 month ago

Gaussian distributed weights for LLMs

The previous post looked at the FP4 4-bit floating point format. This post will look at another 4-bit floating point format, NF4, and higher precision analogs. NF4 and FP4 are comm...

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medium.com /1 day ago

Numerical & Categorical Data Preprocessing in Machine Learning

Introduction to Data PreprocessingContinue reading on Medium »

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medium.com /1 month ago

I Built Linear Regression From Scratch (No scikit-learn). Here’s What I Learned.

Understanding gradient descent by implementing every line of code myself.Continue reading on Medium В»

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medium.com /2 weeks ago

ML learns from data

Machine LearningContinue reading on Medium »

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medium.com /1 month ago

What is Machine Learning?

To understand Machine Learning, we first need to understand Artificial Intelligence (AI). Artificial Intelligence: Artificial Intelligence…Continue reading on Medium »

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medium.com /2 weeks ago

Numpy Meshgrid: The Secret to Loop-Free Math

Understanding torch.meshgrid syntaxContinue reading on Medium »

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medium.com /3 weeks ago

My First Weeks Learning Machine Learning in Korea

I recently started learning Machine Learning. Before this, I heard a lot about AI, but I never really understood what was happening…Continue reading on Medium »

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medium.com /3 weeks ago

Векторы, размерности и пространства признаков: как модели машинного обучения «видят» данные

Если убрать из машинного обучения все сложные слова и модные термины, то Continue reading on Medium »

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medium.com /1 week ago

Machine Learning: How Computers Learn from Data

Machine Learning: How Computers Learn from DataContinue reading on Medium »

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sudhamsr.medium.com /1 month ago

Python for Data Science — Array Shape, Dimensions, and Size

A practical guide to understanding how NumPy arrays are structured — because shape, dimensions, and size are the foundation for indexing…Continue reading on Medium »

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medium.com /1 month ago

Machine Learning

Introduction to Machine LearningContinue reading on Medium »

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medium.com /1 month ago

Optimization Theory and Applications

Theory of Descent Directions -A Mathematical Derivation of Steepest Descent and Newton Steps — 2 (Continued)Continue reading on Medium »

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medium.com /1 month ago

Gradient Descent: Backbone of modern LLM

Optimization is the art of finding the “best” version of something. In mathematics, that often means finding the lowest point of a curve —…Continue reading on Medium »

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medium.com

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towardsdatascience.com

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johndcook.com

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