#linearalgebra

[ww92030]

QR Algorithm. Char belongs to「 BGC_1080#2426 」.

[kennak]

これを見ると、「Dedekind ring」「デデキント環」「デデキント整域」という用語が、/Algebra/DedekindRing の同義語らしいと見当が付きます（リンク先を見ないとホントのところはわかりませんが）。「台集合」という用語が2回出現してますが、片一方は /Algebra/CommRing に関連して、もう一方は /Algebra/LinearAlgebra/Module に関連してです -- 異なる文脈で似たような意味で使われます。 管理された人工的な名前を準備することにより、同義語・多義語の状況がハッキリと把握できます。同義語・多義語の認識と解決は、用語が出現した文脈を見るしかないのですが、上記のような用語索引（と語彙目録の総体）は認識と解決の助けにはなるでしょう。

デジタル語彙目録:: 野生の言葉達と索引 - 檜山正幸のキマイラ飼育記 (はてなBlog)

[laomusicarts]

Getting Started with AI and Machine Learning

LAOMUSIC ARTS 2024 presents

I just finished the learning path “Getting Started with AI and Machine Learning” ! learning path => (7 courses => 9hr 38min)

#lao #music #laomusic #laomusicArts #LAO #MUSIC #LAOMUSIC #LAOMUSIC_ARTS #lao #music #laomusic #laoMusic #laomusicarts #laomusicArts #LAO #MUSIC #ARTS #LAOMUSIC #LAOMUSICARTS #ai #neuralnetworks #artificialneuralnetworks #machinelearning #linearalgebra #artificialintelligence #deeplearning #python #computervision #reinforcementlearning #pytorch

Check it out:

[dat-a-queenblog]

This user was in hyper productive mode today

Am feeling so satisfied and happy that i have completed some task today like for me this is a super hyper productive mode after a very long time ..😅

Task completed

linear Algebra 5.1,5.3,3.3

Probability regression,law of addition , conditional probability ,solved some exercise question

Mobile computing week6 ,week8

Ps: My first final is of Mc but i am taking it slowly becoz i know all the stuff and the theory slides contain like nothing as i know everything from code prespective so i will just go through slides and just read them the main reason is that it has only 2 credit hours and my prof give equal grades to everyone so for like wasting time its better that i will go through the subject which has 3 credit hours so for now i need to go through linear algebra ,prob and db.....

[acid-lich]

This week’s notes

[statisticsexplained]

If you suffer from understanding Slader Linear algebra and Its Applications, then we have solutions for this as well.

The solution is... Statistics Explained, an astounding online tutoring website. They are enabled to help you in understanding the Slader linear algebra and its applications in detail. We offer 24×7 assistance.

[fray-ah]

had my linear algebra exam today, it actually went pretty ok but my brain was actual toast after those 5 hours :-)) got a couple of questions somewhat wrong, but overall it was better than expected. now i only have 1 exam left (in a week) and then i’m done with this semester! been a weird one surely, but that’s what happens when someone decides to eat a sick bat smh

[dronacharyacollege]

The Mathematics of Machine Learning_Dronacharya

People exploring into the world of data science and using Machine Learning (ML) practices to probe statistical... To gain Information Keep reading…

https://blog-gn.dronacharya.info/index.php/the-mathematics-of-machine-learning/

[shoonyamathematics-blog]

#IITJAMMATHEMATICS#CSIRNETMATHEMATICS#SHOONYAMTHEMATICS#LINEARALGEBRA https://www.instagram.com/p/B_XN0skAG7I/?igshid=1avndkjyowi26

[tyler-studies]

NOVEMBER 30th, 2018 🌗

I’m alive! I promise. I’ve just been studying my butt off. I’m doing great in all of my classes. Here’s a picture of me studying linear algebra a few days ago. I post more on my Instagram so feel free to follow me @tyler.fps (fun fact: you’ll actually see my face)

[yasudai]

いま届いた。 複素関数論の次は、半年かけて線形代数を勉強する🧐 #石井俊全 #大学の線形代数 #技術評論社 #藤岡敦 #手を動かしてまなぶ線形代数 #裳華房 #linearalgebra https://www.instagram.com/p/CprS3dEyzrp/?igshid=NGJjMDIxMWI=

[ww92030]

Strassen's Algorithm. Char belongs to「brandonforty2#0420」.

[go-classes]

Students Reviews on "Linear Algebra Revision Lecture" by @sachinmittal28 Sir. @goclasses_cse https://youtu.be/0Sdldf0dTYQ #students #algebra #gatecse #computerscience #linearalgebra https://www.instagram.com/p/CnpMbDOMaYZ/?igshid=NGJjMDIxMWI=

[laomusicarts]

Machine Learning Foundations: Linear Algebra

LAOMUSIC ARTS 2023 presents

I just finished the course “Machine Learning Foundations: Linear Algebra” by Terezija Semenski!

#lao #music #laomusic #laomusicArts #LAO #MUSIC #LAOMUSIC #LAOMUSIC_ARTS #disgustinglyWhite #worstteacherever #machinelearning #linearalgebra

Check it out:

[dat-a-queenblog]

Man i just want to go back to 2020 and just want to slap myself and want to stop myself from choosing cs as my major......🥲

PS : Tomorrow i have a prob exam and am feeling like i dont remember any single formula in the middle of the night what is happening with mee ....

[algebraindatascience]

An Introduction to Linear Algebra for Data Science and Machine Learning

You'll need to comprehend the mathematical concept of linear algebra if you're interested in mastering data science and machine learning. The most crucial mathematical ability in machine learning is linear algebra. Most artificial intelligence models can be written as matrices. A dataset is frequently shown as a matrix. We'll explain linear algebra to you in this blog article and offer you a fundamental idea of what it is and how it functions. We'll talk about linear transformations and demonstrate how data science and machine learning use them. We'll end by offering you some pointers and suggestions on how to begin using linear algebra for data science and machine learning. So continue reading to understand more about linear algebra!

What is linear algebra? 

Though it can be a challenging subject, don't be frightened to start studying it. We'll give you an overview of linear algebra and how it's used in data science and machine learning in this blog article. You ought to be able to comprehend linear algebra and its significance better after reading this. There are several internet resources that can get you started if you're interested in learning more. Don't wait any longer; begin learning right away!

Linear transformations 

Machine learning and data science are two of the most well-liked topics today. However, professionals must be familiar with linear algebra in order to work at their best. For creating predictions and for comprehending how data is handled, this mathematical theory is crucial. The fundamentals of linear algebra will be covered in this article, along with how using it in data science and machine learning applications can benefit you. You will know more about these crucial areas by the end of the article and be able to use linear algebra in your own work. Remain tuned!

FAQ's

What is linear algebra and what are its applications in data science and machine learning? 

Mathematicians who study linear algebra study matrices and linear transformations. For tasks like feature selection, dimensionality reduction, model development, cross-validation, and more, it is employed in data science and machine learning. In data science and machine learning, linear algebra is frequently used for matrix representation (linear transformations), eigenvalue issues, principal component analysis, support vector machines, and other tasks.

What are some of the most common problems that can be solved with linear algebra? 

For data analysis and machine learning, linear algebra can be very useful for a number of reasons. Either eigenvalue issues or systems of equations can be solved using linear algebra. The ability to isolate and solve a particular puzzle piece makes linear algebra extremely helpful in this situation. They can understand the relevant data much more clearly by doing this. Signal processing is another area where linear algebra is frequently used. Here, waveforms or other sorts of signals are transformed or processed using linear algebra. In order to recognize patterns in photographs, linear algebra is also useful in image recognition. Lastly, NLP makes use of linear algebra (Natural Language Processing),

How do I learn more about linear algebra concepts so that I can apply them to my data analysis work? 

Continue reading if you want to understand more about the concepts of linear algebra and how to use them in data analysis. Although we'll be using Python for our examples in this blog series, the same ideas hold true for other programming languages as well. Beginning with the basics, you should know that linear algebra is a collection of mathematical concepts and methods that may be used to tackle issues like CLEANING YOUR DATA, PROJECTING STATISTICS, and UNDERSTANDING HOW MACHINES COMPUTE. Knowing how to utilize linear algebra is crucial for data science and machine learning. In particular, techniques from linear algebra can assist you in resolving issues like: executing matrix operations (such as multiplication and inverse matrix multiplication), vector operations (such as vector addition or vector multiplication), calculating determinants, eigenvalue and singular value decompositions of matrices, resolving systems of linear equations (using the Gaussian elimination algorithm or specific LU factorization methods), and learning how to model probability distributions. So, if you're interested in finding out more about the ideas of data analysis and how they connect to linear algebra, stay reading! We will talk about using matrices in the following post in this series.