#matrices — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #matrices, aggregated by home.social.
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Alright, future engineers!
**Matrix:** A rectangular array of numbers arranged in rows & columns.
Ex: `[[1, 2], [3, 4]]` is a 2x2 matrix.
Pro-Tip: Think of them as structured data, essential for solving systems & linear transformations!
#LinAlg #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix Multiplication:** Combines two matrices, representing sequential linear transformations.
Ex: An (m x n) * (n x p) matrix yields an (m x p) result.
Pro-Tip: The number of columns in the first matrix *must* equal the rows in the second (n=n)!
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix Multiplication:** Combines two matrices, representing sequential linear transformations.
Ex: An (m x n) * (n x p) matrix yields an (m x p) result.
Pro-Tip: The number of columns in the first matrix *must* equal the rows in the second (n=n)!
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix:** A rectangular grid of numbers organized into rows & columns.
Ex: `A = [[1,2],[3,4]]` (2x2).
Pro-Tip: Dimensions (rows x cols) dictate *all* operations! Check `(m x n)` first!
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix:** A rectangular grid of numbers organized into rows & columns.
Ex: `A = [[1,2],[3,4]]` (2x2).
Pro-Tip: Dimensions (rows x cols) dictate *all* operations! Check `(m x n)` first!
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix Transpose:** Swap a matrix's rows & columns.
Ex: `A=[[1,2],[3,4]]`, `A^T=[[1,3],[2,4]]`
Pro-Tip: It's like flipping it over the main diagonal! Great for reorienting data.
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix Transpose:** Swap a matrix's rows & columns.
Ex: `A=[[1,2],[3,4]]`, `A^T=[[1,3],[2,4]]`
Pro-Tip: It's like flipping it over the main diagonal! Great for reorienting data.
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Determinant (2x2):** A scalar indicating how much a matrix transformation scales/flips space.
Ex: For `[[a,b],[c,d]]`, `det = ad - bc`.
Pro-Tip: If `det = 0`, the matrix is *singular* - it squishes space to a line or point!
#Matrices #LinearAlgebra #STEM #StudyNotes -
Alright, future engineers!
**Determinant (2x2):** A scalar indicating how much a matrix transformation scales/flips space.
Ex: For `[[a,b],[c,d]]`, `det = ad - bc`.
Pro-Tip: If `det = 0`, the matrix is *singular* - it squishes space to a line or point!
#Matrices #LinearAlgebra #STEM #StudyNotes -
Alright, future engineers!
**Identity Matrix (I):** A square matrix with 1s on the main diagonal & 0s elsewhere.
Ex: `I = [[1,0],[0,1]]` for 2x2.
Pro-Tip: It's the 1 of matrix multiplication! `A * I = A`. It doesn't change a matrix when multiplied.
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Identity Matrix (I):** A square matrix with 1s on the main diagonal & 0s elsewhere.
Ex: `I = [[1,0],[0,1]]` for 2x2.
Pro-Tip: It's the 1 of matrix multiplication! `A * I = A`. It doesn't change a matrix when multiplied.
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix Multiplication:** A core operation combining matrices. Rows of 1st x Columns of 2nd.
Ex: `[[1,2],[3,4]] * [[5],[6]] = [[1*5+2*6],[3*5+4*6]]`.
Pro-Tip: Inner dimensions MUST match! (e.g., `(m x n) * (n x p)`)
#LinAlg #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Determinant:** A scalar value from a square matrix. It indicates volume scaling & invertibility.
Ex: `det([a,b; c,d]) = ad-bc`.
Pro-Tip: If `det(A) = 0`, matrix A is singular (not invertible)!
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Determinant:** A scalar value from a square matrix. It indicates volume scaling & invertibility.
Ex: `det([a,b; c,d]) = ad-bc`.
Pro-Tip: If `det(A) = 0`, matrix A is singular (not invertible)!
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix Multiplication:** Combining two matrices to get a new one, representing composed transformations.
Ex: `(2x3) * (3x2)` gives a `(2x2)` matrix.
Pro-Tip: Inner dimensions MUST match (n in m x n * n x p)!
#Matrices #LinearAlgebra #STEM #StudyNotes -
Alright, future engineers!
**Matrix Multiplication:** Combining two matrices to get a new one, representing composed transformations.
Ex: `(2x3) * (3x2)` gives a `(2x2)` matrix.
Pro-Tip: Inner dimensions MUST match (n in m x n * n x p)!
#Matrices #LinearAlgebra #STEM #StudyNotes -
Alright, future engineers!
**Matrix Mult:** Rows of 1st dot products with cols of 2nd. NOT element-wise!
Ex: `[[1,2],[3,4]] * [[5],[6]] = [[1*5+2*6], [3*5+4*6]]`
Pro-Tip: Inner dimensions MUST match for product to be defined! (m x n * n x p = m x p)
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
**Matrix Mult:** Rows of 1st dot products with cols of 2nd. NOT element-wise!
Ex: `[[1,2],[3,4]] * [[5],[6]] = [[1*5+2*6], [3*5+4*6]]`
Pro-Tip: Inner dimensions MUST match for product to be defined! (m x n * n x p = m x p)
#LinearAlgebra #Matrices #STEM #StudyNotes -
Alright, future engineers!
The **Determinant** of a square matrix is a scalar value that tells you if the matrix is invertible.
Ex: For `[[a,b],[c,d]]`, `det = ad - bc`.
Pro-Tip: If `det = 0`, the matrix is singular (no inverse), meaning `Ax=b` has no unique solution! -
Alright, future engineers!
The **Determinant** of a square matrix is a scalar value that tells you if the matrix is invertible.
Ex: For `[[a,b],[c,d]]`, `det = ad - bc`.
Pro-Tip: If `det = 0`, the matrix is singular (no inverse), meaning `Ax=b` has no unique solution! -
Alright, future engineers!
**Matrix multiplication** combines two matrices, forming a new one where elements are dot products of rows & columns.
Ex: `(AB)_ij = sum(A_ik * B_kj)`. Inner dimensions must match!
Pro-Tip: It's NOT commutative (AB != BA)!
#Matrices #LinearAlgebra #STEM #StudyNotes -
Alright, future engineers!
**Matrix multiplication** combines two matrices, forming a new one where elements are dot products of rows & columns.
Ex: `(AB)_ij = sum(A_ik * B_kj)`. Inner dimensions must match!
Pro-Tip: It's NOT commutative (AB != BA)!
#Matrices #LinearAlgebra #STEM #StudyNotes -
Thanks for attending! We were able to port a lot more of `glam` into our Gleam matrices library. A polished release will be right around the corner and 1.0 not far off.
See you next week, like/follow/subscribe: https://youtube.com/@seancribbs
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Thanks for attending! We were able to port a lot more of `glam` into our Gleam matrices library. A polished release will be right around the corner and 1.0 not far off.
See you next week, like/follow/subscribe: https://youtube.com/@seancribbs
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Welcome back to another Sunday! Let's polish up the https://hex.pm/packages/matrix_gleam library for a new release. Join me at 1:30PM CDT:
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Welcome back to another Sunday! Let's polish up the https://hex.pm/packages/matrix_gleam library for a new release. Join me at 1:30PM CDT:
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Alright, future engineers!
**Matrix Multiplication** creates a new matrix where `C_ij` is the dot product of row `i` from the 1st matrix & column `j` from the 2nd.
Ex: `A(2x3) * B(3x2)` gives `C(2x2)`.
Pro-Tip: Inner dimensions must match! -
Alright, future engineers!
**Matrix Multiplication** creates a new matrix where `C_ij` is the dot product of row `i` from the 1st matrix & column `j` from the 2nd.
Ex: `A(2x3) * B(3x2)` gives `C(2x2)`.
Pro-Tip: Inner dimensions must match! -
A major #update to my #collection of #morphological #matrices worthy to infer #networks:
a bunch of matrices we generated and reanalysed for a paper by S. Renner, D. Sokoloff, and me dealing with ancestors, hard polytomies and seed plant evolution; depicted not as the usual cladograms (where ancestor-descendant pairs must trigger unsolvable hard trichotomies) but as a "Romerogram" (or spindle graph) that shows #dichotomy as well as #buddingEvolution, i.e., #phylogeny -
A major #update to my #collection of #morphological #matrices worthy to infer #networks:
a bunch of matrices we generated and reanalysed for a paper by S. Renner, D. Sokoloff, and me dealing with ancestors, hard polytomies and seed plant evolution; depicted not as the usual cladograms (where ancestor-descendant pairs must trigger unsolvable hard trichotomies) but as a "Romerogram" (or spindle graph) that shows #dichotomy as well as #buddingEvolution, i.e., #phylogeny -
🎩🤓 Behold, the blog that bravely attempts to explain the mystical Q, K, V #matrices as if they're the holy trinity of #AI. Spoiler alert: it's mostly a glorified game of 'which word matters?' because we all need another reason to overcomplicate common sense. 🙄🔍
https://arpitbhayani.me/blogs/qkv-matrices/ #QKV #Overcomplication #CommonSense #HackerNews #HackerNews #ngated -
🎩🤓 Behold, the blog that bravely attempts to explain the mystical Q, K, V #matrices as if they're the holy trinity of #AI. Spoiler alert: it's mostly a glorified game of 'which word matters?' because we all need another reason to overcomplicate common sense. 🙄🔍
https://arpitbhayani.me/blogs/qkv-matrices/ #QKV #Overcomplication #CommonSense #HackerNews #HackerNews #ngated -
This is how you know your approaching the analytic fried zone : staring at the screen making #Math pirate noises..." Arrrrrr, ya see how this works don't ya ? We'll be squeezin #Convergence from yer scrawny little Rows and Columns, yer big fat #Matrices and anything else that gets in Captains way. We're gonna hang yer #Asymptotes out to dry in the broiling sun. We're gonna scrub the decks with yer residuals and paint the cannons with yer remainders arrrrrrr"
So yes. Possibly time for a break...
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🚨BREAKING NEWS: Another thrilling episode of "Linear Algebra for Masochists" has been released, now with 100% more convoluted Python snippets! 🙃 Get ready to snooze through endless chapters of #vectors and #matrices, because clearly, that's what the world needs right now! 📉📚
https://little-book-of.github.io/linear-algebra/books/en-US/lab.html #LinearAlgebraForMasochists #PythonSnippets #TechHumor #BreakingNews #HackerNews #ngated -
🚨BREAKING NEWS: Another thrilling episode of "Linear Algebra for Masochists" has been released, now with 100% more convoluted Python snippets! 🙃 Get ready to snooze through endless chapters of #vectors and #matrices, because clearly, that's what the world needs right now! 📉📚
https://little-book-of.github.io/linear-algebra/books/en-US/lab.html #LinearAlgebraForMasochists #PythonSnippets #TechHumor #BreakingNews #HackerNews #ngated -
Prisms Math
** SUBSCRIPTION REQUIRED (US $23.99/year)**
- VR content modules for students to deepen their understanding of core concepts
#Fractions
#Ratios
#Rate_of_Change
#Probability
#Surface_Area#Algebra
#Geometry
#Advanced Algebra
#ComplexNumbers
#PolarCoordinates
#VectorAddition
#Ellipses
#Matriceshttps://www.meta.com/experiences/app/8875714305775804/?utm_source=oculus&utm_medium=share
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Prisms Math
** SUBSCRIPTION REQUIRED (US $23.99/year)**
- VR content modules for students to deepen their understanding of core concepts
#Fractions
#Ratios
#Rate_of_Change
#Probability
#Surface_Area#Algebra
#Geometry
#Advanced Algebra
#ComplexNumbers
#PolarCoordinates
#VectorAddition
#Ellipses
#Matriceshttps://www.meta.com/experiences/app/8875714305775804/?utm_source=oculus&utm_medium=share
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There are two obstacles when you use #Assemblyscript and #Deno for #MachineLearning #ML and #SmallLanguageModels #SLM. 1. Assemblyscript works with linear memory and you have to flatten 3D #tensors and 2D #matrices into 1D #arrays for doing any matrix algebra. 2. Deno is a bit more complicated than Node.js when working with #WASM (#Webassemly) files. Here is how you do it. Webassembly makes NNUE models even faster in computation. This together with #WebGPU will become our future. #AI #javascript
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Furthermore, she showed that certain stabilising effects of #network #structure can only be reproduced in theoretical #matrices when the underlying distribution of interaction strengths is highly skewed - which is in apparent contradiction to what is suggested by random matrix theory....
👉🏻 https://www.biorxiv.org/content/10.1101/2024.01.25.577181v2.abstract
More of her results will be published soon, so stay tuned!
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BLOSUM (Genetics 🧬)
In bioinformatics, the BLOSUM matrix is a substitution matrix used for sequence alignment of proteins. BLOSUM matrices are used to score alignments between evolutionarily divergent protein sequences. They are based on local alignments. BLOSUM matrices were first introduced in a paper by Steven Henikoff and Jorja Henikoff. They scanned the BLOCKS database for very conserved regions of p...
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¿Quieres saber cuántas #matrices tiene tu manuscrito? Es muy útil para pedir presupuesto de #corrección, #editing, #maquetación o #InformesDeLectura.
Da igual si usas #Word, #GoogleDocs o #Scrivener, la respuesta está aquí 👇
https://s.mtrbio.com/mafjmikgij -
'A Statistical Experimental Design Method for Constructing Deterministic Sensing Matrices for Compressed Sensing', by Youran Qi, Xu He, Tzu-Hsiang Hung, Peter Chien.
http://jmlr.org/papers/v25/22-0760.html
#sensing #matrices #compressed -
Pour s'amuser avec les matrices et #geogebra: 3 exemples pour générer des motifs aléatoires de Truchet.
https://www.geogebra.org/m/hap9nyjs#chapter/1127720
#matrix #maths #teaching #TeachingMaths #matrices #MathsExpertes
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For all Transf ∈ ℝn×n, the matrix is invertible if and only if rank(Transf) = n
The #determinant exists if and only if the transformation matrix is square.
The determinant in a linear transformation is the (signed) area of the image of the fundamental basis formed by the unit square.#algebra #matrices #tutorial #determinants #singularity #math #maths #mathematics #mathStodon #ML #machineLearning #systems
