Latest updates for Linear Algebra

Fresh curated links around Linear algebra are collected here so marketers can spot useful updates and turn timely ideas into posts faster.

Recent items include:

  • SC29: Dealing with в€ћ
  • Machine Learning Chapter 3.3: Ordinary Least Squares (OLS) Normal Equation & Geometry
  • The Spectral Theorem Turns Symmetric Matrices Into Simple Scaling

Post angles to try

Share the most useful takeaway for your audience.
Turn one article into a quick practical checklist.
Ask your audience how this shift affects their work.
Turn angles into scheduled posts

Fresh articles and ideas

Recent curated links from global sources. Generate one free draft from any story, then use SocialBu to schedule and refine your content calendar.

medium.com /1 month ago

SC29: Dealing with в€ћ

Find the whole linear algebra series hereContinue reading on Medium »

Read source
medium.com /1 month ago

Machine Learning Chapter 3.3: Ordinary Least Squares (OLS) Normal Equation & Geometry

PART 11: ORDINARY LEAST SQUARES (OLS)Continue reading on Medium »

Read source
pub.towardsai.net /6 days ago

The Spectral Theorem Turns Symmetric Matrices Into Simple Scaling

How a transformation splits into independent componentsContinue reading on Towards AI »

Read source
medium.com /1 month ago

Math for ML — Part 3: From Dot Product to Projection

In the previous article, we explored the dot product.Continue reading on Medium »

Read source
johndcook.com /1 month ago

Patented application of linear algebra

I just found out Brian Beckman and I got a patent on work we did for GSI Technology [1]. Nearly all the work I do is under an NDA, so I don’t often get a chance to talk about my pr...

Read source
johndcook.com /1 month ago

Computing a lower bound on matrix rank

Suppose you want to know the rank of an n × n matrix A, the number of linearly independent rows of A, or equivalently the number of linearly independent columns. There are at least...

Read source
johndcook.com /1 month ago

Proof of the rank-trace theorem

The previous post discussed the motivation for and application of the rank-trace theorem. This post will give a proof. Suppose A is a real symmetric matrix. The rank-trace inequali...

Read source
medium.com /1 week ago

LLM Coding Interview Series: 1/30 [Tensor Thinking]

NumPy and PyTorch Skills Every LLM Interview TestsContinue reading on Models to Systems »

Read source
habr.com /1 month ago

Как фундаментальная математика помогает переосмыслить градиентный спуск

Градиентный спуск — стандартный инструмент обучения нейросетей. Но с точки зрения линейной алгебры операция обновления весов выглядит некорректно: мы складываем вектор и ковектор....

Read source
mql5.com /1 month ago

Signal Matrix Lab: user manual

Read source
medium.com /1 month ago

Machine Learning Chapter 3.4: Multicollinearity, Assumptions & Python Implementations

PART 14: MULTICOLLINEARITYContinue reading on Medium »

Read source
medium.com /2 weeks ago

Why Can’t Transformers Just Rearrange Their Matrix Multiplications?

Linear attention unlocks a matrix multiplication trick that standard attention can't use, but the speedup comes at a cost.Continue reading on Medium »

Read source

Turn fresh research into a full content calendar

Use SocialBu to discover ideas, generate post drafts, and schedule them across your social channels.

Sources covering Linear Algebra

habr.com

Recent coverage from public sources
Public source

medium.com

Recent coverage from public sources
Public source

johndcook.com

Recent coverage from public sources
Public source

mql5.com

Recent coverage from public sources
Public source