Linear algebra смотреть последние обновления за сегодня на .

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Learn Linear Algebra in this 20-hour college course. Watch the second half here: 🤍 This course is taught by Dr. Jim Hefferon, a professor of mathematics at St Michael's College. 📔 The course follows along with Dr. Hefferon's Linear Algebra text book. The book is available for free: 🤍 📚 Access additional course resources at: 🤍 🔗 Stephen Chew's Learning How to Learn series: 🤍 🔗 3Blue1Brown's Linear Algebra series: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction to Linear Algebra by Hefferon ⌨️ (0:04:35) One.I.1 Solving Linear Systems, Part One ⌨️ (0:26:08) One.I.1 Solving Linear Systems, Part Two ⌨️ (0:40:56) One.I.2 Describing Solution Sets, Part One ⌨️ (0:54:21) One.I.2 Describing Solution Sets, Part Two ⌨️ (1:02:48) One.I.3 General = Particular + Homogeneous ⌨️ (1:18:33) One.II.1 Vectors in Space ⌨️ (1:35:08) One.II.2 Vector Length and Angle Measure ⌨️ (1:51:31) One.III.1 Gauss-Jordan Elimination ⌨️ (2:00:00) One.III.2 The Linear Combination Lemma ⌨️ (2:44:32) Two.I.1 Vector Spaces, Part One ⌨️ (3:08:12) Two.I.1 Vector Spaces, Part Two ⌨️ (3:33:01) Two.I.2 Subspaces, Part One ⌨️ (3:58:16) Two.I.2 Subspaces, Part Two ⌨️ (4:23:43) Two.II.1 Linear Independence, Part One ⌨️ (4:45:11) Two.II.1 Linear Independence, Part Two ⌨️ (5:03:57) Two.III.1 Basis, Part One ⌨️ (5:23:55) Two.III.1 Basis, Part Two ⌨️ (5:42:34) Two.III.2 Dimension ⌨️ (6:03:24) Two.III.3 Vector Spaces and Linear Systems ⌨️ (6:25:09) Three.I.1 Isomorphism, Part One ⌨️ (6:54:08) Three.I.1 Isomorphism, Part Two ⌨️ (7:21:47) Three.I.2 Dimension Characterizes Isomorphism ⌨️ (7:43:43) Three.II.1 Homomorphism, Part One ⌨️ (8:14:52) Three.II.1 Homomorphism, Part Two ⌨️ (8:30:24) Three.II.2 Range Space and Null Space, Part One ⌨️ (9:00:17) Three.II.2 Range Space and Null Space, Part Two. ⌨️ (9:20:57) Three.II Extra Transformations of the Plane ⌨️ (9:52:06) Three.III.1 Representing Linear Maps, Part One. ⌨️ (10:13:18) Three.III.1 Representing Linear Maps, Part Two ⌨️ (10:34:18) Three.III.2 Any Matrix Represents a Linear Map ⌨️ (10:58:32) Three.IV.1 Sums and Scalar Products of Matrices ⌨️ (11:19:14) Three.IV.2 Matrix Multiplication, Part One ⌨️ Three.IV.2 Matrix Multiplication, Part Two (We accidentally left this section out. Watch it here: 🤍 The following sections are in the second video: 🤍 ⌨️ Three.IV.3 Mechanics of Matrix Multiplication ⌨️ Three.IV.4 Matrix Inverse, Part One ⌨️ Three.IV.4 Matrix Inverse, Part Two ⌨️ Three.V.1 Changing Vector Representations ⌨️ Three.V.2 Changing Map Representations, Part One ⌨️ Three.V.2 Changing Map Representations, Part Two ⌨️ Three.VI Projection ⌨️ Four.I.1 Determinants ⌨️ Four.I.3 Permutation Expansion, Part One ⌨️ Four.I.3 Permutation Expansion, Part Two ⌨️ Four.I.4 Determinants Exist (optional) ⌨️ Four.II.1 Geometry of Determinants ⌨️ Four.III.1 Laplace's formula for the determinant ⌨️ Five.I.1 Complex Vector Spaces ⌨️ Five.II.1 Similarity ⌨️ Five.II.2 Diagonalizability ⌨️ Five.II.3 Eigenvalues and Eigenvectors, Part One ⌨️ Five.II.3 Eigenvalues and Eigenvectors, Part Two ⌨️ Five.II.3 Geometry of Eigenvalues and Eigenvectors Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍

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Beginning the linear algebra series with the basics. Help fund future projects: 🤍 An equally valuable form of support is to simply share some of the videos. Home page: 🤍 Correction: 6:52, the screen should show [x1, y1] + [x2, y2] = [x1+x2, y1+y2] Full series: 🤍 Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. 🤍 If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. Music: 🤍 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: 🤍 Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍

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Full episode with Gilbert Strang (Nov 2019): 🤍 New clips channel (Lex Clips): 🤍 Once it reaches 20,000 subscribers, I'll start posting the clips there instead. (more links below) For now, new full episodes are released once or twice a week and 1-2 new clips or a new non-podcast video is released on all other days. Clip from full episode: 🤍 If you enjoy these clips, subscribe to the new clips channel (Lex Clips): 🤍 Once it reaches 20,000 subscribers, I'll start posting the clips there instead. For now, new full episodes are released once or twice a week and 1-2 new clips or a new non-podcast video is released on all other days. (more links below) Podcast full episodes playlist: 🤍 Podcasts clips playlist: 🤍 Podcast website: 🤍 Podcast on Apple Podcasts (iTunes): 🤍 Podcast on Spotify: 🤍 Podcast RSS: 🤍 Gilbert Strang is a professor of mathematics at MIT and perhaps one of the most famous and impactful teachers of math in the world. His MIT OpenCourseWare lectures on linear algebra have been viewed millions of times. Subscribe to this YouTube channel or connect on: - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍 - Medium: 🤍 - Support on Patreon: 🤍

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Home page: 🤍 This introduces the "Essence of linear algebra" series, aimed at animating the geometric intuitions underlying many of the topics taught in a standard linear algebra course. Error corrections: - At one point I mistakenly allude to calculators using the Taylor expansion of sine for its computations, when in reality most use CORDIC (or something like it). - Around 30 seconds in, there is a typo in how the determinant is written, which should be ad - bc Full series: 🤍 Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. 🤍 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: 🤍 Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍

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This is the start of a one semester university level course on Linear Algebra that emphasizes both conceptual understanding as well as procedural fluency with the techniques of Linear Algebra. In this video, we get to see just the beginning of some of the big ideas of Linear Algebra. FULL PLAYLIST: 🤍 Linear Algebra is a story told in two worlds, one geometric and one algebraic. The interplay between the two is beautiful and powerful. In this video we introduce the notion of a linear transformation and see how this simplification makes our lives far easier. Now it's your turn: 1) Summarize the big idea of this video in your own words 2) Write down anything you are unsure about to think about later 3) What questions for the future do you have? Where are we going with this content? 4) Can you come up with your own sample test problem on this material? Solve it! Learning mathematics is best done by actually DOING mathematics. A video like this can only ever be a starting point. I might show you the basic ideas, definitions, formulas, and examples, but to truly master calculus means that you have to spend time - a lot of time! - sitting down and trying problems yourself, asking questions, and thinking about mathematics. So before you go on to the next video, pause and go THINK. This video is part of a Linear Algebra course taught by Dr. Trefor Bazett BECOME A MEMBER: ►Join: 🤍 MATH BOOKS & MERCH I LOVE: ► My Amazon Affiliate Shop: 🤍

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This video provides a basic outline for how we will go about studying linear algebra by attempting to answer the question: What is Linear Algebra? Sources: The example of a linear system, as well as the terms 'row picture' and 'column picture,' are taken from Gilbert Strang's Introduction to Linear Algebra text and supplemental materials.

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Sign up with brilliant and get 20% off your annual subscription: 🤍 STEMerch Store: 🤍 Support the Channel: 🤍 PayPal(one time donation): 🤍 ►Follow me Instagram: 🤍 Twitter: 🤍 3D Software Used Runiter (for the actual graphs): 🤍 Geogebra (used for vectors, & is free): 🤍 Animations: Brainup Studios ( 🤍 ) ►My Setup: Space Pictures: 🤍 Magnetic Floating Globe: 🤍 Camera: 🤍 Mic: 🤍 Tripod: 🤍 Equilibrium Tube: 🤍 ►Check out the my Amazon Store: 🤍

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Learn 10 essential math concepts for software engineering and technical interviews. Understand how programmers use mathematics in fields like AI, game dev, crypto, machine learning, and more. #math #programming #top10 💬 Chat with Me on Discord 🤍 🔗 Resources Computer Science 101 🤍 Cryptography for Developers 🤍 Technical Interview Prep 🤍 📚 Chapters 🔥 Get More Content - Upgrade to PRO Upgrade at 🤍 Use code YT25 for 25% off PRO access 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font 🔖 Topics Covered - Do programmers need math? - Math tutorial for programming - Machine learning math - Do computer hackers use math? - Linear algebra for programmers - Boolean Algebra explained - Combinatorics explained - How does Big-O notation work

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MIT RES.18-009 Learn Differential Equations: Up Close with Gilbert Strang and Cleve Moler, Fall 2015 View the complete course: 🤍 Instructor: Gilbert Strang A matrix produces four subspaces: column space, row space (same dimension), the space of vectors perpendicular to all rows (the nullspace), and the space of vectors perpendicular to all columns. License: Creative Commons BY-NC-SA More information at 🤍 More courses at 🤍

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👉🏻 Sign up for Our Complete Data Science Training with 57% OFF: 🤍 Why is linear algebra actually useful? There very many applications of linear algebra. In data science, in particular, there are several ones of high importance. Some are easy to grasp, others not just yet. In this lesson, we will explore 3 of them: • Vectorized code also known as array programming • Image recognition • Dimensionality reduction Okay. Let’s start from the simplest and probably the most commonly used one – vectorized code. We can certainly claim that the price of a house depends on its size. Suppose you know that the exact relationship for some neighborhood is given by the equation: Price equals 10,190 + 223 times size. Moreover, you know the sizes of 5 houses 693, 656, 1060, 487, and 1275 square feet. What you want to do is plug-in each size in the equation and find the price of each house, right? Well, for the first one we get: 10190 + 223 times 693 equals 164,729. Then we can find the next one, and so on, until we find all prices. Now, if we have 100 houses, doing that by hand would be quite tedious, wouldn’t it? One way to deal with that problem is by creating a loop. You can iterate over the sizes, multiplying each of them by 223, and adding 10,190. However, we are smarter than that, aren’t we? We know some linear algebra already. Let’s explore these two objects: A 5 by 2 matrix and a vector of length 2. The matrix contains a column of 1s and another – with the sizes of the houses. The vector contains 10,190 and 223 – the numbers from the equation. If we go about multiplying them, we will get a vector of length 5. The first element will be equal to: 1 times 10,190 plus 693 times 223. The second to: 1 times 10,190 plus 656 times 223. And so on. ► Consider hitting the SUBSCRIBE button if you LIKE the content: 🤍 ► VISIT our website: 🤍 🤝 Connect with us LinkedIn: 🤍 365 Data Science is an online educational career website that offers the incredible opportunity to find your way into the data science world no matter your previous knowledge and experience. We have prepared numerous courses that suit the needs of aspiring BI analysts, Data analysts and Data scientists. We at 365 Data Science are committed educators who believe that curiosity should not be hindered by inability to access good learning resources. This is why we focus all our efforts on creating high-quality educational content which anyone can access online. Check out our Data Science Career guides: 🤍 #LinearAlgebra #Math #DataScience

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MIT 18.06 Linear Algebra, Spring 2005 Instructor: Gilbert Strang View the complete course: 🤍 YouTube Playlist: 🤍 1. The Geometry of Linear Equations License: Creative Commons BY-NC-SA More information at 🤍 More courses at 🤍

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A quick review of basic matrix operations.

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Speakers: Gilbert Strang, Alan Edelman, Pavel Grinfeld, Michel Goemans Revered mathematics professor Gilbert Strang capped a 61-year career as a faculty member at MIT by delivering his final 18.06 Linear Algebra lecture before retiring at the age of 88. In addition to a brief review for the course final exam, the overflowing audience (both in person and on the live YouTube stream) heard recollections, appreciations, and congratulations from Prof. Strang’s colleagues and former students. A rousing standing ovation concluded this historic event. 0:00 - Seating 8:56 - Class start 10:05 - Alan Edelman's speech about Gilbert Strang 12:57 - Gilbert Strang's introduction 15:42 - Solving linear equations 30:42 - Visualization of four-dimensional space 31:37 - Nonzero Solutions 32:16 - Finding Solutions 34:19 - Elimination Process 41:05 - Introduction to Equations 42:26 - Finding Solutions 44:29 - Solution 1 47:42 - Rank of the Matrix 51:39 - In appreciation of Gilbert Strang 52:08 - Congratulations on retirement 52:38 - Personal experiences with Strang 55:01 - Life lessons learned from Strang 1:01:08 - Gil Strang's impact on math education 1:01:44 - Gil Strang's teaching style 1:02:20 - Gil Strang's legacy 1:03:23 - Congratulations to Gil Strang This video has been dubbed using an artificial voice via 🤍 to increase accessibility. You can change the audio track language in the Settings menu. License: Creative Commons BY-NC-SA More information at 🤍 More courses at 🤍 Support OCW at 🤍 We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at 🤍

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This Algebra video tutorial provides a basic introduction into linear equations. It discusses the three forms of a linear equation - the point slope form, the slope intercept form, and the standard form of the equation. This video explains how to calculate the slope of a line that passes through two points and how to graph a linear equation in all 3 forms. This video also discusses parallel and perpendicular lines. It contains plenty of examples and practice problems. Get The Full 1 Hour 30 Minute Video: 🤍 Direct Link to The Full Video: 🤍 How To Pass Algebra: 🤍 Full 1 Hour 30 Minute Video: 🤍 Join The Membership Program: 🤍 Access My Video Playlists: 🤍 My E-book: How To Pass Difficult Math & Science Classes: 🤍 Here is a list of topics: 00:15 - Slope Intercept Form 01:00 - Standard Form and Point Slope Form 02:12 - Slope - Rise over Run 05:40 - How To Calculate The Slope Between Two Points 06:54 - How To Find The X-Intercept and Y-Intercept 10:41 - Parallel and Perpendicular Lines 14:47 - How To Graph Linear Equations In Slope Intercept Form 19:31 - How To Graph Linear Functions In Standard Form 23:05 - How To Graph Linear Equations In Point Slope Form 28:21 - How To Graph Horizontal and Vertical Lines 29:08 - Undefined Slope Disclaimer: Some of the links associated with this video may generate affiliate commissions on my behalf. As an amazon associate, I earn from qualifying purchases that you may make through such affiliate links.

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In this session of Machine Learning Tech Talks, Tai-Danae Bradley, Postdoc at X, the Moonshot Factory, will share a few ideas for linear algebra that appear in the context of Machine Learning. Chapters: 0:00 - Introduction 1:37 - Data Representations 15:02 - Vector Embeddings 31:52 - Dimensionality Reduction 37:11 - Conclusion Resources: Google Developer’s ML Crash Course on Collaborative Filtering → 🤍 Eigenvectors and Eigenvalues” by 3Blue1Brown → 🤍 Introduction to Linear Algebra” (5th ed) by Gilbert Strang → 🤍 Catch more ML Tech Talks → 🤍 Subscribe to TensorFlow → 🤍

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In this video I will briefly show you one of my math books. This book is great for people who want to learn linear algebra. It is called Elementary Linear Algebra and it was written by Howard Anton. Here it is: 🤍 Useful Math Supplies 🤍 My Recording Gear 🤍 (these are my affiliate links) *Math, Physics, and Computer Science Books* Epic Math Book List 🤍 Pre-algebra, Algebra, and Geometry 🤍 College Algebra, Precalculus, and Trigonometry 🤍 Probability and Statistics 🤍 Discrete Mathematics 🤍 Proof Writing 🤍 Calculus 🤍 Differential Equations Books 🤍 Partial Differential Equations Books 🤍 Linear Algebra 🤍 Abstract Algebra Books 🤍 Real Analysis/Advanced Calculus 🤍 Complex Analysis 🤍 Number Theory 🤍 Graph Theory 🤍 Topology 🤍 Graduate Level Books 🤍 Computer Science 🤍 Physics 🤍 These are my affiliate links. As an Amazon Associate I earn from qualifying purchases. If you enjoyed this video please consider liking, sharing, and subscribing. Udemy Courses Via My Website: 🤍 Free Homework Help : 🤍 My FaceBook Page: 🤍 There are several ways that you can help support my channel:) Consider becoming a member of the channel: 🤍 My GoFundMe Page: 🤍 My Patreon Page: 🤍 Donate via PayPal: 🤍 Udemy Courses(Please Use These Links If You Sign Up!)* Abstract Algebra Course 🤍 Advanced Calculus Course 🤍 Calculus 1 Course 🤍 Calculus 2 Course 🤍 Calculus 3 Course 🤍 Calculus 1 Lectures with Assignments and a Final Exam 🤍 Calculus Integration Insanity 🤍 Differential Equations Course 🤍 Differential Equations Lectures Course (Includes Assignments + Final Exam) 🤍 College Algebra Course 🤍 How to Write Proofs with Sets Course 🤍 How to Write Proofs with Functions Course 🤍 Trigonometry 1 Course 🤍 Trigonometry 2 Course 🤍 Statistics with StatCrunch Course 🤍 Math Graduate Programs, Applying, Advice, Motivation 🤍 Daily Devotionals for Motivation with The Math Sorcerer 🤍 Thank you:)

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Online courses with practice exercises, text lectures, solutions, and exam practice: 🤍 We introduce vector spaces in linear algebra. #LinearAlgebra #Vectors #AbstractAlgebra LIKE AND SHARE THE VIDEO IF IT HELPED! Visit our website: 🤍 Subscribe on YouTube: 🤍 Like us on Facebook: 🤍 Submit your questions on Reddit: 🤍 #LinearAlgebra #Algebra #UniversityMath #Lecture *Playlists* Linear Algebra: 🤍 *Recommended Textbooks* Linear Algebra and Its Applications (Lay): 🤍 Linear Algebra Done Right (Axler): 🤍 Introduction to Linear Algebra (Strang): 🤍 Linear Algebra: Step by Step (Singh): 🤍 3,000 Solved Problems in Linear Algebra (Lipschutz): 🤍 In this video we talk about Vector Spaces and ask ourselves if some sets are vector spaces. We also talk about the polynomial vector space. Hello, welcome to TheTrevTutor. I'm here to help you learn your college courses in an easy, efficient manner. If you like what you see, feel free to subscribe and follow me for updates. If you have any questions, leave them below. I try to answer as many questions as possible. If something isn't quite clear or needs more explanation, I can easily make additional videos to satisfy your need for knowledge and understanding.

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Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now: 🤍 Introduction to linear subspaces of Rn Watch the next lesson: 🤍 Missed the previous lesson? 🤍 Linear Algebra on Khan Academy: Have you ever wondered what the difference is between speed and velocity? Ever try to visualize in four dimensions or six or seven? Linear algebra describes things in two dimensions, but many of the concepts can be extended into three, four or more. Linear algebra implies two dimensional reasoning, however, the concepts covered in linear algebra provide the basis for multi-dimensional representations of mathematical reasoning. Matrices, vectors, vector spaces, transformations, eigenvectors/values all help us to visualize and understand multi dimensional concepts. This is an advanced course normally taken by science or engineering majors after taking at least two semesters of calculus (although calculus really isn't a prereq) so don't confuse this with regular high school algebra. About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Linear Algebra channel:: 🤍 Subscribe to KhanAcademy: 🤍

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This precalculus video tutorial provides a basic introduction into the gaussian elimination - a process that involves elementary row operations with 3x3 matrices which allows you to solve a system of linear equations with 3 variables. You need to convert the system of equations into an augmented matrix and use matrix row operations to write it in row echelon form. Next, you can convert back into a system of linear equations and solve using back substitution. This video contains plenty of examples and practice problems. Math and Science Video Playlists: 🤍 My Twitter Page: 🤍 Solving Systems of Equations Using Excel's Solver Tool: 🤍 Solving Systems of Equations With Cramer's Rule: 🤍 How To Solve a System With 4 Equations: 🤍

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The fundamental concepts of span, linear combinations, linear dependence, and bases. Help fund future projects: 🤍 An equally valuable form of support is to simply share some of the videos. Home page: 🤍 Full series: 🤍 Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. 🤍 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: 🤍 Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍

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How to Learn Linear Algebra, The Right Way? This is the book on amazon: 🤍 (note this is my affiliate link, I earn a small percentage from qualifying purchases) In this video I talk about a Linear Algebra book with a very interesting title. I have known about this book for years but I never actually sat down and carefully looked at it until now. This book is awesome! The book is called Linear Algebra Done Right and is written by Sheldon Axler. Please leave any comments or questions in the comment section below. Thank you:)

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Linear algebra is central to almost all areas of mathematics. For instance, linear algebra is fundamental in modern presentations of geometry, including for defining basic objects such as lines, planes and rotations. Also, functional analysis may be basically viewed as the application of linear algebra to spaces of functions. Linear algebra is also used in most sciences and engineering areas, because it allows modeling many natural phenomena, and efficiently computing with such models. ⭐️ Table of Contents ⭐ (0:00) Linear Algebra - Systems of Linear Equations (1 of 3) (16:20) Linear Algebra - System of Linear Equations (2 of 3) (27:55) Linear Algebra - Systems of Linear Equations (3 of 3) (47:18) Linear Algebra - Row Reduction and Echelon Forms (1 of 2) (54:49) Linear Algebra - Row Reduction and Echelon Forms (2 of 2) (1:4:10) Linear Algebra - Vector Equations (1 of 2) (1:14:05) Linear Algebra - Vector Equations (2 of 2) (1:24:54) Linear Algebra - The Matrix Equation Ax = b (1 of 2) (1:39:21) Linear Algebra - The Matrix Equation Ax = b (2 of 2) (1:44:48) Linear Algebra - Solution Sets of Linear Systems (1:57:49) Linear Algebra - Linear Independence (2:11:20) Linear Algebra - Linear Transformations (1 of 2) (2:25:10) Linear Algebra - Linear Transformations (2 of 2) (2:39:19) Linear Algebra - Matrix Operations (2:56:24) Linear Algebra - Matrix Inverse (3:12:17) Linear Algebra - Invertible Matrix Properties (3:24:24) Linear Algebra - Determinants (1 of 2) (3:44:40) Linear Algebra - Determinants (2 of 2) (4:04:28) Linear Algebra - Cramer's Rule (4:18:20) Linear Algebra - Vector Spaces and Subspaces (1 of 2) (4:48:30) Linear Algebra - Vector Spaces and Subspaces (5:13:13) Linear Algebra - Null Spaces, Column Spaces, and Linear Transformations (5:33:25) Linear Algebra - Basis of a Vector Space (5:59:43) Linear Algebra - Coordinate Systems in a Vector Space (6:15:41) Linear Algebra - Dimension of a Vector Space (6:26:35) Linear Algebra - Rank of a Matrix (6:50:09) Linear Algebra - Markov Chains (7:09:23) Linear Algebra - Eigenvalues and Eigenvectors (7:32:03) Linear Algebra - Matrix Diagonalization (7:49:08) Linear Algebra - Inner Product, Vector Length, Orthogonality

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This is just a few minutes of a complete course. Get full lessons & more subjects at: 🤍. In this lesson the student will learn how to work with matrices in linear algebra. We will discuss the fundamental concept of a matrix, then explore more detailed topics including the elements of the matrix and the matrix transpose. All of these topics are important in a linear algebra course,

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This precalculus video tutorial provides a basic introduction into matrices. It covers matrix notation and how to determine the order of a matrix and the value of the elements inside a matrix. This video also covers the addition and subtraction of matrices as well as the scalar multiplication of matrices. Precalculus New Video Playlist: 🤍 My E-Book: 🤍 Video Playlists: 🤍 Homework Help: 🤍 Subscribe: 🤍 Support & Donations: 🤍 Youtube Membership: 🤍 Disclaimer: Some of the links associated with this video may generate affiliate commissions on my behalf. As an amazon associate, I earn from qualifying purchases that you may make through such affiliate links.

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University of Oxford mathematician Dr Tom Crawford explains how to calculate the eigenvalues and eigenvectors of a matrix, with 2 fully worked examples. Check out ProPrep with a 30-day free trial to see how it can help you to improve your performance in STEM-based subjects: 🤍 Test your understanding with some practice exercises courtesy of ProPrep. You can download the workbooks and solutions for free here: 🤍 You can also find several video lectures from ProPrep explaining the topic further here: 🤍 And fully worked video solutions from ProPrep instructors are here: 🤍 Watch other videos from the Oxford Linear Algebra series at the links below. Solving Systems of Linear Equations using Elementary Row Operations (ERO’s): 🤍 Calculating the inverse of 2x2, 3x3 and 4x4 matrices: 🤍 What is the Determinant Function: 🤍 The Easiest Method to Calculate Determinants: 🤍 The video begins by introducing the eigenvalue equation which we are trying to solve, with a discussion of possible methods of solution. We see that the only way a non-zero eigenvector can be found is if the determinant of the characteristic matrix is zero, which gives us the characteristic equation, or characteristic polynomial. Solving this equal to zero gives the eigenvalues, which are then substituted back into the eigenvalue equation to give the corresponding eigenvectors. The method is demonstrated first with a 2x2 matrix example, and then for a 3x3 matrix. In both cases we consider a general eigenvector before choosing one parameter to make the final vector as simple as possible. Produced by Dr Tom Crawford at the University of Oxford. Tom is an Early-Career Teaching and Outreach Fellow at St Edmund Hall: 🤍 For more maths content check out Tom's website 🤍 You can also follow Tom on Facebook, Twitter and Instagram 🤍tomrocksmaths. 🤍 🤍 🤍 Get your Tom Rocks Maths merchandise here: 🤍

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Linear Algebra: Introduction to Vectors

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Our latest student lecture features the first lecture in the second term (1st Year) introductory course on Linear Algebra from leading Oxford Mathematician James Maynard. We are making these lectures available to give an insight in to the student experience and how we teach. All first and second year lectures are followed by tutorials where students meet their tutor to go through the lecture and associated problem sheet and to talk and think more about the maths. Third and fourth year lectures are followed by classes. You can also watch many other student lectures via our main Student Lectures playlist (also check out specific student lectures playlists): 🤍

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28.10.2022

Ax = b slander for all math enjoyers. F for all course repeaters. Subscribe for more slanders. Also, buy my crypto calendar: 🤍 #slander #maths #linearalgebra #algebra #memes #meme

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00:15:46

09.10.2009

Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now: 🤍 Introduction to linear dependence and independence Watch the next lesson: 🤍 Missed the previous lesson? 🤍 Linear Algebra on Khan Academy: Have you ever wondered what the difference is between speed and velocity? Ever try to visualize in four dimensions or six or seven? Linear algebra describes things in two dimensions, but many of the concepts can be extended into three, four or more. Linear algebra implies two dimensional reasoning, however, the concepts covered in linear algebra provide the basis for multi-dimensional representations of mathematical reasoning. Matrices, vectors, vector spaces, transformations, eigenvectors/values all help us to visualize and understand multi dimensional concepts. This is an advanced course normally taken by science or engineering majors after taking at least two semesters of calculus (although calculus really isn't a prereq) so don't confuse this with regular high school algebra. About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Linear Algebra channel:: 🤍 Subscribe to KhanAcademy: 🤍

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00:13:24

29.08.2017

Learn More at mathantics.com Visit 🤍 for more Free math videos and additional subscription based content!

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00:15:44

16.08.2021

Animated computer graphics are based on models composed of thousands of tiny primitive shapes such as triangles, and each vertex in a model is encoded as a vector. This computer science video demonstrates how matrices are used to transform these vector. In particular you will learn how to derive a two dimensional rotation matrix using trigonometric identities, and how to transform a model in two dimensional space with a scaling matrix, a translation matrix and a rotation matrix. You will also learn how several transformation matrices can be combined using matrix multiplication to create a single transformation matrix that encodes multiple transformations at once. Chapters: 00:00 Introduction to 3D computer models 00:50 Scale a vector with a vector 02:10 Translate a vector with a vector 02:40 Derive a rotation matrix using trigonometric identities 07:42 Rotate a vector with a matrix 08:48 Scale a vector with a matrix 09:32 Scale and rotate a vector with a single matrix 10:42 2D translation matrices 13:06 Translation, rotation and scaling combined 15:05 The role of a graphics processing unit (GPU)

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07.05.2016

Online courses with practice exercises, text lectures, solutions, and exam practice: 🤍 We talk about the subspace of a vector space. LIKE AND SHARE THE VIDEO IF IT HELPED! Visit our website: 🤍 Subscribe on YouTube: 🤍 Like us on Facebook: 🤍 Submit your questions on Reddit: 🤍 #LinearAlgebra #Algebra #UniversityMath #Lecture *Playlists* Linear Algebra: 🤍 *Recommended Textbooks* Linear Algebra and Its Applications (Lay): 🤍 Linear Algebra Done Right (Axler): 🤍 Introduction to Linear Algebra (Strang): 🤍 Linear Algebra: Step by Step (Singh): 🤍 3,000 Solved Problems in Linear Algebra (Lipschutz): 🤍 In this video we talk about subspaces and prove that the span is a subspace. Hello, welcome to TheTrevTutor. I'm here to help you learn your college courses in an easy, efficient manner. If you like what you see, feel free to subscribe and follow me for updates. If you have any questions, leave them below. I try to answer as many questions as possible. If something isn't quite clear or needs more explanation, I can easily make additional videos to satisfy your need for knowledge and understanding.

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01:21:31

14.08.2019

Linear algebra is the branch of mathematics concerning linear equations such as linear functions and their representations through matrices and vector spaces. Linear algebra is central to almost all areas of mathematics. In this course you will learn most of the basics of linear algebra which will help to understand better and apply in ML as well. Topic covered Introduction to Vectors (0:00) Length of a Vector in 2 Dimensions (examples) (06:58) Vector Addition (11:55) Multiplying a Vector by a Scalar (16:38) Vector Subtraction (19:32) Vectors with 3 components (3 dimensions) (22:27) Length of a 3-Dimensional Vector (26:05) Definition of R^n (34:00) Length of a Vector (40:37) Proof: Vector Addition is Commutative and Associative (42:14) Algebraic Properties of Vectors (49:59) Definition of the Dot Product (51:33) Dot Product - Angle Between Two Vectors (55:15) Find the Angle Between Two Vectors (example) (01:4:41) Orthogonal Vectors (1:08:26) Proof about the Diagonals of a Parellelogram (01:12:47)

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15.09.2016

A visual understanding of eigenvectors, eigenvalues, and the usefulness of an eigenbasis. Help fund future projects: 🤍 An equally valuable form of support is to simply share some of the videos. Home page: 🤍 Full series: 🤍 Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. 🤍 A solution to the puzzle at the end: 🤍 Typo: At 12:27, "more that a line full" should be "more than a line full". 3blue1brown is a channel about animating math, in all senses of the word animate. Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍

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00:06:58

01.09.2019

Denkanstoß zu Vektoren, Matrizen, Linearkombinationen Wenn noch spezielle Fragen sind: 🤍 Playlists zu allen Mathe-Themen findet ihr auf der Startseite unter: 🤍 E-Books, Onlinekurse und Skripte für Mathe findet ihr hier: 🤍 Alle Infos und Kontakte von mir: 🤍 Daniel Jung erklärt Mathe in Kürze. Lernkonzept: Mathe lernen durch kurze, auf den Punkt gebrachte Videos zu allen Themen für Schule und Studium, sortiert in Themenplaylists für eine intuitive Channelnavigation. #MathebyDanielJung #Matrix #Linearkombination

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01:12:37

06.01.2015

MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: 🤍 Instructor: Choongbum Lee This lecture is a review of the linear algebra needed for the course, including matrices, linear transformations, eigenvalue, and eigenvectors. License: Creative Commons BY-NC-SA More information at 🤍 More courses at 🤍

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00:07:31

21.01.2014

Linear algebra studies the dynamics of the simplest possible interactions among multiple variables. Its fundamentals are essential to all areas of mathematics.

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00:00:59

05.09.2021

#Math #Calculus #Calc1 #Physics #Trigonometry #Integrals #Antiderivatives #DiffEQ #Engineering #Mathematics #LinnearAlgebra #Derivatives #Science #Physics #Highschool #College #NicholasGKK #Shorts

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00:50:08

10.02.2022

Recorded Monday, February 7. A second course in linear algebra covering vector spaces and matrix decompositions taught by Dr. Anthony Bosman. Full Course: 🤍 The lectures closely follow 'Advanced Linear and Matrix Algebra' by Johnston: 🤍 Subscribe: 🤍 Learn more about the Andrews University math department: 🤍

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