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#linearalgebra

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2something<p><span>Bilinear forms implies the existence of alinear forms, heterolinear forms, homolinear forms, panlinear forms, and polylinear forms. All of which are generalized by queerlinear forms.<br><br></span><a href="https://transfem.social/tags/LinearAlgebra" rel="nofollow noopener" target="_blank">#LinearAlgebra</a> <a href="https://transfem.social/tags/BilinearForm" rel="nofollow noopener" target="_blank">#BilinearForm</a> <a href="https://transfem.social/tags/QueerlinearForm" rel="nofollow noopener" target="_blank">#QueerlinearForm</a></p>
Eric Maugendre<p>Logistic regression may be used for classification.</p><p>In order to preserve the convex nature for the loss function, a log-loss cost function has been designed for logistic regression. This cost function extremes at labels True and False.</p><p>The gradient for the loss function of logistic regression comes out to have the same form of terms as the gradient for the Least Squared Error.</p><p>More: <a href="https://www.baeldung.com/cs/gradient-descent-logistic-regression" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">baeldung.com/cs/gradient-desce</span><span class="invisible">nt-logistic-regression</span></a></p><p><a href="https://hachyderm.io/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a> <a href="https://hachyderm.io/tags/algebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>algebra</span></a> <a href="https://hachyderm.io/tags/linearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearAlgebra</span></a> <a href="https://hachyderm.io/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a> <a href="https://hachyderm.io/tags/maths" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>maths</span></a> <a href="https://hachyderm.io/tags/mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mathematics</span></a> <a href="https://hachyderm.io/tags/mathStodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mathStodon</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/dataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataScience</span></a> <a href="https://hachyderm.io/tags/machineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machineLearning</span></a> <a href="https://hachyderm.io/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a> <a href="https://hachyderm.io/tags/neuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralNetworks</span></a> <a href="https://hachyderm.io/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NLP</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/models" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>models</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/logisticRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>logisticRegression</span></a> <a href="https://hachyderm.io/tags/logLoss" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>logLoss</span></a> <a href="https://hachyderm.io/tags/sigmoid" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sigmoid</span></a> <a href="https://hachyderm.io/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classification</span></a> <a href="https://hachyderm.io/tags/differentialCalculus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>differentialCalculus</span></a> <a href="https://hachyderm.io/tags/loss" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>loss</span></a></p>
katch wreck<p>`His initial intended uses were for linguistic analysis and other mathematical subjects like card shuffling, but both Markov chains and matrices rapidly found use in other fields.` </p><p><a href="https://en.wikipedia.org/wiki/Stochastic_matrix#History" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.wikipedia.org/wiki/Stochast</span><span class="invisible">ic_matrix#History</span></a></p><p><a href="https://mastodon.social/tags/AndreyMarkov" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AndreyMarkov</span></a> <a href="https://mastodon.social/tags/Markov" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Markov</span></a> <a href="https://mastodon.social/tags/MarkovChain" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MarkovChain</span></a> <a href="https://mastodon.social/tags/MarkovModel" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MarkovModel</span></a> <a href="https://mastodon.social/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://mastodon.social/tags/stochastic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stochastic</span></a> <a href="https://mastodon.social/tags/stochasticProcess" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stochasticProcess</span></a> <a href="https://mastodon.social/tags/randomWalk" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomWalk</span></a> <a href="https://mastodon.social/tags/statisticalPhysics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statisticalPhysics</span></a> <a href="https://mastodon.social/tags/physics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>physics</span></a> <a href="https://mastodon.social/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inference</span></a> <a href="https://mastodon.social/tags/distribution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distribution</span></a> <a href="https://mastodon.social/tags/equilibrium" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>equilibrium</span></a> <a href="https://mastodon.social/tags/transitionRate" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>transitionRate</span></a> <a href="https://mastodon.social/tags/transitionMatrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>transitionMatrix</span></a> <a href="https://mastodon.social/tags/MarkovMatrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MarkovMatrix</span></a> <a href="https://mastodon.social/tags/stochasticMatrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stochasticMatrix</span></a> <a href="https://mastodon.social/tags/linearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearAlgebra</span></a> <a href="https://mastodon.social/tags/differentialEquation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>differentialEquation</span></a> <a href="https://mastodon.social/tags/equation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>equation</span></a></p>
Hacker News<p>Graphical Linear Algebra</p><p><a href="https://graphicallinearalgebra.net/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">graphicallinearalgebra.net/</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HackerNews</span></a> <a href="https://mastodon.social/tags/Graphical" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Graphical</span></a> <a href="https://mastodon.social/tags/Linear" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Linear</span></a> <a href="https://mastodon.social/tags/Algebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Algebra</span></a> <a href="https://mastodon.social/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a> <a href="https://mastodon.social/tags/Graphics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Graphics</span></a> <a href="https://mastodon.social/tags/DataVisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataVisualization</span></a> <a href="https://mastodon.social/tags/MathEducation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MathEducation</span></a></p>
datatofu<p>This is called "A Gentle Introduction to the Hessian Matrix"</p><p>Hessians are somewhere between <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> <a href="https://mastodon.social/tags/calculus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>calculus</span></a> and <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> but still a core aspect of <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> </p><p>All in all, building and deriving things like these are probably only useful when developing a unique solution. For the vast majority of cases, having a general understanding is enough. </p><p>... actually, I am pretty sure that there is a <a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> library for just such an occasion (I have never looked though so ymmv)</p>
datatofu<p>Okay. After that bit of hilarity yesterday, have some stuff on <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> </p><p>Not a formula sheet but still useful for developing your <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> intuition</p>
Alex Nelson<p>Here's a question: let \(M\) be a \(0\times 0\) matrix with entries in the field \(\mathbb{F}\). What is \(\det(M)\)?</p><p><a href="https://mathstodon.xyz/tags/Mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Mathematics</span></a> <a href="https://mathstodon.xyz/tags/Determinant" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Determinant</span></a> <a href="https://mathstodon.xyz/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a> <a href="https://mathstodon.xyz/tags/Matrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Matrix</span></a></p>
datatofu<p><a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> cheatsheets for <a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://mastodon.social/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> <a href="https://mastodon.social/tags/calculus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>calculus</span></a> and <a href="https://mastodon.social/tags/scipy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scipy</span></a> </p><p>(Not necessarily in that order)</p>
Benjohn<p>Can anyone suggest good tutorials for learning an intuition and working with the exterior product of vectors and multi-vectors in general? </p><p>Having access to an oriented area (and other oriented objects beyond just directions) seems very powerful. </p><p>I've been trying to learn it and show my kids some bits of it, as it seems potentially more learnable than just "vector algebra": there's more there to connect together – the concepts are reinforced by carrying through in to more places?</p><p>Anyway – a pedagogical approach to this would be splendid. Thanks!</p><p><a href="https://todon.nl/tags/maths" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>maths</span></a> <a href="https://todon.nl/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a></p>
Hacker News<p>Starting with Two Matrices [pdf]</p><p><a href="https://web.mit.edu/18.06/www/Essays/starting2matrices.pdf" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">web.mit.edu/18.06/www/Essays/s</span><span class="invisible">tarting2matrices.pdf</span></a></p><p><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HackerNews</span></a> <a href="https://mastodon.social/tags/StartingWithTwoMatrices" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>StartingWithTwoMatrices</span></a> <a href="https://mastodon.social/tags/Matrices" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Matrices</span></a> <a href="https://mastodon.social/tags/PDF" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PDF</span></a> <a href="https://mastodon.social/tags/MITResearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MITResearch</span></a> <a href="https://mastodon.social/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a> <a href="https://mastodon.social/tags/MathEducation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MathEducation</span></a></p>
illestpreacha<p>LinesUponLines</p><p>Video: <a href="https://www.youtube.com/watch?v=3U5lon28Zgk" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/watch?v=3U5lon28Zg</span><span class="invisible">k</span></a></p><p>Blog: <a href="https://blog.illestpreacha.com/wccclines" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">blog.illestpreacha.com/wccclin</span><span class="invisible">es</span></a></p><p><a href="https://post.lurk.org/tags/WCCC" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>WCCC</span></a> <a href="https://post.lurk.org/tags/wccchallenge" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>wccchallenge</span></a> <a href="https://post.lurk.org/tags/Livecoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Livecoding</span></a> <a href="https://post.lurk.org/tags/CreativeCoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CreativeCoding</span></a> <a href="https://post.lurk.org/tags/Lines" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Lines</span></a> <a href="https://post.lurk.org/tags/collages" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>collages</span></a></p><p>For this week's Creative Code challenge by <span class="h-card" translate="no"><a href="https://genart.social/@sableraph" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>sableraph</span></a></span> : “ Only Lines ”, LinesUponLines uses Line Based equations/functions in <a href="https://post.lurk.org/tags/SonicPi" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SonicPi</span></a> &amp; <a href="https://post.lurk.org/tags/LiveCodelab" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LiveCodelab</span></a>.</p><p>The Point to Plane and Line to Plane equations in the SonicPi Code were previously used in <a href="https://post.lurk.org/tags/Genuary2024" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Genuary2024</span></a>, where these <a href="https://post.lurk.org/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> components were used to create the sound.</p><p>While the LiveCodeLab sketch contains various collages of the same piece of code.</p><p><a href="https://post.lurk.org/tags/Poetry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Poetry</span></a></p><p>Lines Upon Lines<br>Lines Across<br>Lines Underneath<br>Lines Wanting to speak<br>Lines itching to creep<br>Lines willing to weep<br>Lines embracing a leak<br>Lines feeling a loss<br>Just Lines Upon Lines</p><p><a href="https://post.lurk.org/tags/creativecoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>creativecoding</span></a> <a href="https://post.lurk.org/tags/coding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>coding</span></a> <a href="https://post.lurk.org/tags/soundscape" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>soundscape</span></a> <a href="https://post.lurk.org/tags/worldbuilding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>worldbuilding</span></a><br><a href="https://post.lurk.org/tags/scifi" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scifi</span></a> <a href="https://post.lurk.org/tags/animation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>animation</span></a> <a href="https://post.lurk.org/tags/glitchart" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>glitchart</span></a></p>
Petr Nuska<p><a href="https://mastodon.world/tags/AcademicJob" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicJob</span></a> | <a href="https://mastodon.world/tags/PhDStudentship" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PhDStudentship</span></a> </p><p>@ Queen Mary University of London</p><p>Understanding Learning Dynamics of Neural Audio Models Using Linear Algebra.</p><p>Research focuses on Music Source Separation, Low Rank matrices, and Mechanistic Interpretability in Digital Audio.</p><p><a href="https://www.c4dm.eecs.qmul.ac.uk/news/2024-11-12.PhD-call-2025/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">c4dm.eecs.qmul.ac.uk/news/2024</span><span class="invisible">-11-12.PhD-call-2025/</span></a></p><p>Deadline: 29/01/2025</p><p>CC <span class="h-card" translate="no"><a href="https://a.gup.pe/u/academicjobs" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>academicjobs</span></a></span> </p><p><a href="https://mastodon.world/tags/MusicScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MusicScience</span></a> <a href="https://mastodon.world/tags/ComputationalMusicProcessing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalMusicProcessing</span></a> <a href="https://mastodon.world/tags/ComputationalMusicology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalMusicology</span></a> <a href="https://mastodon.world/tags/neuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralnetworks</span></a> <a href="https://mastodon.world/tags/MusicTech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MusicTech</span></a> <a href="https://mastodon.world/tags/DigitalAudio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DigitalAudio</span></a> <a href="https://mastodon.world/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a></p>
Eddi Silva<p>This is the official repository for The Hundred-Page Language Models Book by Andriy Burkov</p><p><a href="https://www.thelmbook.com" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">thelmbook.com</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://mastodon.social/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a> <a href="https://mastodon.social/tags/software" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>software</span></a> <a href="https://mastodon.social/tags/softwaredevelopment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>softwaredevelopment</span></a> <a href="https://mastodon.social/tags/development" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>development</span></a> <a href="https://mastodon.social/tags/developers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>developers</span></a> <a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://mastodon.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://mastodon.social/tags/learn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learn</span></a> <a href="https://mastodon.social/tags/learning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learning</span></a> <a href="https://mastodon.social/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a> <a href="https://mastodon.social/tags/arrays" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>arrays</span></a> <a href="https://mastodon.social/tags/matrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>matrix</span></a> <a href="https://mastodon.social/tags/vector" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>vector</span></a> <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> <a href="https://mastodon.social/tags/llm" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>llm</span></a> <a href="https://mastodon.social/tags/largelanguagemodels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>largelanguagemodels</span></a> <a href="https://mastodon.social/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlp</span></a></p>
Patrick Honner<p>What are some nice ways to think about the fact that if the columns of a square matrix are pairwise orthogonal unit vectors, then the rows of the matrix must also be pairwise orthogonal unit vectors?</p><p><a href="https://mathstodon.xyz/tags/Math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Math</span></a> <a href="https://mathstodon.xyz/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a></p>
Eddi Silva<p>Giskard</p><p>Giskard is a **holistic Testing platform for AI models** to control all 3 types of AI risks: Quality, Security &amp; Compliance.</p><p><a href="https://www.giskard.ai" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">giskard.ai</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://mastodon.social/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a> <a href="https://mastodon.social/tags/software" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>software</span></a> <a href="https://mastodon.social/tags/softwaredevelopment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>softwaredevelopment</span></a> <a href="https://mastodon.social/tags/development" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>development</span></a> <a href="https://mastodon.social/tags/developers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>developers</span></a> <a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://mastodon.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://mastodon.social/tags/learn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learn</span></a> <a href="https://mastodon.social/tags/learning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learning</span></a> <a href="https://mastodon.social/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a> <a href="https://mastodon.social/tags/arrays" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>arrays</span></a> <a href="https://mastodon.social/tags/matrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>matrix</span></a> <a href="https://mastodon.social/tags/vector" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>vector</span></a> <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> <a href="https://mastodon.social/tags/llm" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>llm</span></a> <a href="https://mastodon.social/tags/largelanguagemodels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>largelanguagemodels</span></a> <a href="https://mastodon.social/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlp</span></a></p>
Eddi Silva<p>Ragas</p><p><a href="https://docs.ragas.io/en/latest/getstarted/evals/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">docs.ragas.io/en/latest/getsta</span><span class="invisible">rted/evals/</span></a></p><p>Ragas is a library that provides tools to supercharge the evaluation of Large Language Model (LLM) applications. It is designed to help you evaluate your LLM applications with ease and confidence.</p><p><a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://mastodon.social/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a> <a href="https://mastodon.social/tags/software" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>software</span></a> <a href="https://mastodon.social/tags/softwaredevelopment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>softwaredevelopment</span></a> <a href="https://mastodon.social/tags/development" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>development</span></a> <a href="https://mastodon.social/tags/developers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>developers</span></a> <a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://mastodon.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://mastodon.social/tags/learn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learn</span></a> <a href="https://mastodon.social/tags/learning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learning</span></a> <a href="https://mastodon.social/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a> <a href="https://mastodon.social/tags/arrays" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>arrays</span></a> <a href="https://mastodon.social/tags/matrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>matrix</span></a> <a href="https://mastodon.social/tags/vector" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>vector</span></a> <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> <a href="https://mastodon.social/tags/llm" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>llm</span></a> <a href="https://mastodon.social/tags/largelanguagemodels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>largelanguagemodels</span></a> <a href="https://mastodon.social/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlp</span></a></p>
Towards Data Science<p>Matrix algebra doesn’t have to be overwhelming. Jaroslaw Drapala's newest article is designed to help you understand the core concepts of matrix computation, especially for machine learning. </p><p><a href="https://me.dm/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a> <a href="https://me.dm/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a></p><p><a href="https://towardsdatascience.com/how-to-interpret-matrix-expressions-transformations-a5e6871cd224" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/how-to-</span><span class="invisible">interpret-matrix-expressions-transformations-a5e6871cd224</span></a></p>
Methylzero<p>If you had to do a lot of dense linear algebra (QR eigenvalues, SVD, linear least squares, etc.) on modern AMD *CPUs*, which library would you choose for maximum performance? <a href="https://mast.hpc.social/tags/HPC" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HPC</span></a> <a href="https://mast.hpc.social/tags/BLAS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BLAS</span></a> <a href="https://mast.hpc.social/tags/LAPACK" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LAPACK</span></a> <a href="https://mast.hpc.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearalgebra</span></a> <a href="https://mast.hpc.social/tags/NumericalSimulation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NumericalSimulation</span></a> <a href="https://mast.hpc.social/tags/amd" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>amd</span></a></p>
Patrick Honner<p>I noticed something interesting (and perhaps obvious!) while playing around with linear equations this morning.</p><p>When trying to the find the equation of a circle through three points using a system of equations, the strategy is to plug each point into</p><p>\[x^2+y^2+Ax+By+C=0\]</p><p>and to solve the resulting 3x3 system for \( A, B, \) and \(C\). Suppose two points on the circle are \( (2,3) \) and \( (4,4) \). When you plug these in, you get the two equations</p><p>\[ 2A + 3B + C = -13\]<br>\[ 4A + 4B + C = -32\]</p><p>Combining these equations gives you</p><p>\[2A + B = -19\]</p><p>Notice that the slope of the line between the two points, \( \frac{1}{2} \) is encoded in the coefficients of this new equation: it's the coefficient of \(B\) divided by the coefficient of \(A\).</p><p>This is one way to see why three collinear points don't determine a circle. When you plug in all three points to get the system of equations, the fact that the slopes between pairs of points are all the same produces a dependence on the left-hand side of the system that isn't consistent with the right-hand side. For example, if the third point is \( (10,7) \) (which is collinear with the points \( (2,3) \) and \( (4,4) \)), the 3x3 system can be reduced to the system</p><p>\[2A + B = -19\]<br>\[6A + 3B = -117 \]</p><p>which is inconsistent.</p><p>I'm sure this can be probably be understood in a cool way by thinking about duality, or the relationship between affine and linear functions, but I found this simple approach satisfying!</p><p><a href="https://mathstodon.xyz/tags/Math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Math</span></a> <a href="https://mathstodon.xyz/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a> <a href="https://mathstodon.xyz/tags/Mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Mathematics</span></a></p>
Pustam | पुस्तम | পুস্তম🇳🇵<p>Linear Algebra &amp; Calculus needed for Machine Learning.<br><a href="https://mathstodon.xyz/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearAlgebra</span></a> <a href="https://mathstodon.xyz/tags/Calculus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Calculus</span></a> <a href="https://mathstodon.xyz/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://mathstodon.xyz/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://mathstodon.xyz/tags/Math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Math</span></a></p>