Podcasts about Linear algebra

Episode: 2557 Linear algebra, the mathematics behind Google's ranking algorithm.  Today, let's talk about how Google ranks your search results.

Episode: 2544 How humans and computers recognize faces.  Today, UH math professor Krešo Josić recognizes your face.

In this episode of the Neil Ashton podcast, Professor Michael Mahoney discusses the intersection of machine learning, mathematics, and computer science. The conversation covers topics such as randomized linear algebra, foundational models for science, and the debate between physics-informed and data-driven approaches. Prof. Mahoney shares insights on the relevance of his research, the potential of using randomness in algorithms, and the evolving landscape of machine learning in scientific disciplines. He also discusses the evolution and practical applications of randomized linear algebra in machine learning, emphasizing the importance of randomness and data availability. He explores the tension between traditional scientific methods and modern machine learning approaches, highlighting the need for collaboration across disciplines. Prof Mahoney also addresses the challenges of data licensing and the commercial viability of machine learning solutions, offering insights for aspiring researchers in the field.Prof. Mahoney website: https://www.stat.berkeley.edu/~mmahoney/Google scholar: https://scholar.google.com/citations?user=QXyvv94AAAAJ&hl=enYoutube version: https://youtu.be/lk4lvKQsqWUChapters00:00 Introduction to the Podcast and Guest05:51 Understanding Randomized Linear Algebra19:09 Foundational Models for Science32:29 Physics-Informed vs Data-Driven Approaches38:36 The Practical Application of Randomized Linear Algebra39:32 Creative Destruction in Linear Algebra and Machine Learning40:32 The Role of Randomness in Scientific Machine Learning41:56 Identifying Commonalities Across Scientific Domains42:52 The Horizontal vs. Vertical Application of Machine Learning44:19 The Challenge of Common Architectures in Science46:31 Data Availability and Licensing Issues50:04 The Future of Foundation Models in Science54:21 The Commercial Viability of Machine Learning Solutions58:05 Emerging Opportunities in Scientific Machine Learning01:00:24 Navigating Academia and Industry in Machine Learning01:11:15 Advice for Aspiring Scientific Machine Learning ResearchersKeywordsmachine learning, randomized linear algebra, foundational models, physics-informed neural networks, data-driven science, computational efficiency, academic advice, numerical methods, AI in science, engineering, Randomized Linear Algebra, Machine Learning, Scientific Computing, Data Availability, Foundation Models, Academia, Industry, Research, Algorithms, Innovation

Episode: 2514 Today, UH math professor Krešo Josić talks about math and your movie choice.  How Netflix uses linear algebra to determine what movies you will like best.

This is a recap of the top 10 posts on Hacker News on May 11th, 2024.This podcast was generated by wondercraft.ai(00:34): Immersive Linear Algebra (2015)Original post: https://news.ycombinator.com/item?id=40329388&utm_source=wondercraft_ai(02:06): Why the CORDIC algorithm lives rent-free in my headOriginal post: https://news.ycombinator.com/item?id=40326563&utm_source=wondercraft_ai(03:56): PeaZip: Open-source file compression and encryption softwareOriginal post: https://news.ycombinator.com/item?id=40327631&utm_source=wondercraft_ai(05:51): Metabolism of autism reveals developmental originsOriginal post: https://news.ycombinator.com/item?id=40328616&utm_source=wondercraft_ai(07:42): Vision Transformers Need RegistersOriginal post: https://news.ycombinator.com/item?id=40329675&utm_source=wondercraft_ai(09:10): Lessons learned reinventing the Python notebookOriginal post: https://news.ycombinator.com/item?id=40327543&utm_source=wondercraft_ai(11:11): Unix Viruses 25th Anniversary EditionOriginal post: https://news.ycombinator.com/item?id=40327236&utm_source=wondercraft_ai(12:26): She was accused of faking incriminating video of cheerleaders. Nothing was fakeOriginal post: https://news.ycombinator.com/item?id=40327578&utm_source=wondercraft_ai(14:13): Virtualizing the 6502 on a 6502 with 6o6Original post: https://news.ycombinator.com/item?id=40331886&utm_source=wondercraft_ai(16:05): All of the bases in DNA and RNA have now been found in meteoritesOriginal post: https://news.ycombinator.com/item?id=40329777&utm_source=wondercraft_ai This is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Math-to-English Cheat Sheet, published by nahoj on April 9, 2024 on LessWrong. Say you've learnt math in your native language which is not English. Since then you've also read math in English and you appreciate the near universality of mathematical notation. Then one day you want to discuss a formula in real life and you realize you don't know how to pronunce "an". Status: I had little prior knowledge of the topic. This was mostly generated by ChatGPT4 and kindly reviewed by @TheManxLoiner. General Distinguishing case F,δ "Big F" or "capital F", "little delta" Subscripts an "a sub n" or, in most cases, just "a n" Calculus Pythagorean Theorem a2+b2=c2 "a squared plus b squared equals c squared." Area of a Circle A=πr2 "Area equals pi r squared." Slope of a Line m=y2y1x2x1 "m equals y 2 minus y 1 over x 2 minus x 1." Quadratic Formula x=bb24ac2a "x equals minus b [or 'negative b'] plus or minus the square root of b squared minus four a c, all over two a." Sum of an Arithmetic Series S=n2(a1+an) "S equals n over two times a 1 plus a n." Euler's Formula eiθ=cos(θ)+isin(θ) "e to the i theta equals cos [pronounced 'coz'] theta plus i sine theta." Law of Sines sin(A)a=sin(B)b=sin(C)c "Sine A over a equals sine B over b equals sine C over c." Area of a Triangle (Heron's Formula) A=s(sa)(sb)(sc), where s=a+b+c2 "Area equals the square root of s times s minus a times s minus b times s minus c, where s equals a plus b plus c over two." Compound Interest Formula A=P(1+rn)nt "A equals P times one plus r over n to the power of n t." Logarithm Properties logb(xy)=logb(x)+logb(y) Don't state the base if clear from context: "Log of x y equals log of x plus log of y." Otherwise "Log to the base b of x y equals log to the base b of x plus log to the base b of y." More advanced operations Derivative of a Function dfdx or ddxf(x) or f'(x) "df by dx" or "d dx of f of x" or "f prime of x." Second Derivative d2dx2f(x) or f''(x) "d squared dx squared of f of x" or "f double prime of x." Partial Derivative (unreviewed) xf(x,y) "Partial with respect to x of f of x, y." Definite Integral baf(x)dx "Integral from a to b of f of x dx." Indefinite Integral (Antiderivative) f(x)dx "Integral of f of x dx." Line Integral (unreviewed) Cf(x,y)ds "Line integral over C of f of x, y ds." Double Integral badcf(x,y)dxdy "Double integral from a to b and c to d of f of x, y dx dy." Gradient of a Function f "Nabla f" or "gradient of f" to distinguish from other uses such as divergence or curl. Divergence of a Vector Field F "Nabla dot F." Curl of a Vector Field F "Nabla cross F." Laplace Operator (unreviewed) Δf or 2f "Delta f" or "Nabla squared f." Limit of a Function limxaf(x) "Limit as x approaches a of f of x." Linear Algebra (vectors and matrices) Vector Addition v+w "v plus w." Scalar Multiplication cv "c times v." Dot Product vw "v dot w." Cross Product vw "v cross w." Matrix Multiplication AB "A B." Matrix Transpose AT "A transpose." Determinant of a Matrix |A| or det(A) "Determinant of A" or "det A". Inverse of a Matrix A1 "A inverse." Eigenvalues and Eigenvectors λ for eigenvalues, v for eigenvectors "Lambda for eigenvalues; v for eigenvectors." Rank of a Matrix rank(A) "Rank of A." Trace of a Matrix tr(A) "Trace of A." Vector Norm v "Norm of v" or "length of v". Orthogonal Vectors vw=0 "v dot w equals zero." With numerical values Matrix Multiplication with Numerical Values Let A=(1234) and B=(5678), then AB=(19224350). "A B equals nineteen, twenty-two; forty-three, fifty." Vector Dot Product Let v=(1,2,3) and w=(4,5,6), then vw=32. "v dot w equals thirty-two." Determinant of a Matrix For A=(1234), |A|=2. "Determinant of A equals minus two." Eigenvalues and Eigenvectors with Numerical Values Given A=(2112), it has eigenvalues λ1=3 and λ2=1, with corresponding eigenvectors v1=(11) and v2=(11). "Lambda ...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Math-to-English Cheat Sheet, published by nahoj on April 9, 2024 on LessWrong. Say you've learnt math in your native language which is not English. Since then you've also read math in English and you appreciate the near universality of mathematical notation. Then one day you want to discuss a formula in real life and you realize you don't know how to pronunce "an". Status: I had little prior knowledge of the topic. This was mostly generated by ChatGPT4 and kindly reviewed by @TheManxLoiner. General Distinguishing case F,δ "Big F" or "capital F", "little delta" Subscripts an "a sub n" or, in most cases, just "a n" Calculus Pythagorean Theorem a2+b2=c2 "a squared plus b squared equals c squared." Area of a Circle A=πr2 "Area equals pi r squared." Slope of a Line m=y2y1x2x1 "m equals y 2 minus y 1 over x 2 minus x 1." Quadratic Formula x=bb24ac2a "x equals minus b [or 'negative b'] plus or minus the square root of b squared minus four a c, all over two a." Sum of an Arithmetic Series S=n2(a1+an) "S equals n over two times a 1 plus a n." Euler's Formula eiθ=cos(θ)+isin(θ) "e to the i theta equals cos [pronounced 'coz'] theta plus i sine theta." Law of Sines sin(A)a=sin(B)b=sin(C)c "Sine A over a equals sine B over b equals sine C over c." Area of a Triangle (Heron's Formula) A=s(sa)(sb)(sc), where s=a+b+c2 "Area equals the square root of s times s minus a times s minus b times s minus c, where s equals a plus b plus c over two." Compound Interest Formula A=P(1+rn)nt "A equals P times one plus r over n to the power of n t." Logarithm Properties logb(xy)=logb(x)+logb(y) Don't state the base if clear from context: "Log of x y equals log of x plus log of y." Otherwise "Log to the base b of x y equals log to the base b of x plus log to the base b of y." More advanced operations Derivative of a Function dfdx or ddxf(x) or f'(x) "df by dx" or "d dx of f of x" or "f prime of x." Second Derivative d2dx2f(x) or f''(x) "d squared dx squared of f of x" or "f double prime of x." Partial Derivative (unreviewed) xf(x,y) "Partial with respect to x of f of x, y." Definite Integral baf(x)dx "Integral from a to b of f of x dx." Indefinite Integral (Antiderivative) f(x)dx "Integral of f of x dx." Line Integral (unreviewed) Cf(x,y)ds "Line integral over C of f of x, y ds." Double Integral badcf(x,y)dxdy "Double integral from a to b and c to d of f of x, y dx dy." Gradient of a Function f "Nabla f" or "gradient of f" to distinguish from other uses such as divergence or curl. Divergence of a Vector Field F "Nabla dot F." Curl of a Vector Field F "Nabla cross F." Laplace Operator (unreviewed) Δf or 2f "Delta f" or "Nabla squared f." Limit of a Function limxaf(x) "Limit as x approaches a of f of x." Linear Algebra (vectors and matrices) Vector Addition v+w "v plus w." Scalar Multiplication cv "c times v." Dot Product vw "v dot w." Cross Product vw "v cross w." Matrix Multiplication AB "A B." Matrix Transpose AT "A transpose." Determinant of a Matrix |A| or det(A) "Determinant of A" or "det A". Inverse of a Matrix A1 "A inverse." Eigenvalues and Eigenvectors λ for eigenvalues, v for eigenvectors "Lambda for eigenvalues; v for eigenvectors." Rank of a Matrix rank(A) "Rank of A." Trace of a Matrix tr(A) "Trace of A." Vector Norm v "Norm of v" or "length of v". Orthogonal Vectors vw=0 "v dot w equals zero." With numerical values Matrix Multiplication with Numerical Values Let A=(1234) and B=(5678), then AB=(19224350). "A B equals nineteen, twenty-two; forty-three, fifty." Vector Dot Product Let v=(1,2,3) and w=(4,5,6), then vw=32. "v dot w equals thirty-two." Determinant of a Matrix For A=(1234), |A|=2. "Determinant of A equals minus two." Eigenvalues and Eigenvectors with Numerical Values Given A=(2112), it has eigenvalues λ1=3 and λ2=1, with corresponding eigenvectors v1=(11) and v2=(11). "Lambda ...

In this week's episode, host Anna Rose (https://twitter.com/annarrose) and co-host Kobi Gurkan (https://twitter.com/kobigurk) chat with Alex Evans (https://twitter.com/alexhevans) and Guillermo Angeris (https://twitter.com/GuilleAngeris) about their new research paper on Succinct Proofs in Linear Algebra (https://angeris.github.io/papers/zk-linalg.pdf). The paper introduces a framework that simplifies the construction of succinct proofs and offers a toolkit of useful techniques. Their conversation also covers the use of randomized reductions in zero-knowledge proofs, the security of the FRI protocol, and the potential applications of the framework in other systems. Here's some additional links for this episode: *Papers [Succinct Proofs in Linear Algebra by Evans and Angeris](https://angeris.github.io/papers/zk-linalg.pdf Algebraic Reductions of Knowledge by Kothapalli and Parno (https://eprint.iacr.org/2022/009) Proximity Testing with Logarithmic Randomness by Diamond and Posen (https://eprint.iacr.org/2023/630) Ligero: Lightweight Sublinear Arguments Without a Trusted Setup by Ames, Hazay, Ishai and Venkitasubramaniam (https://acmccs.github.io/papers/p2087-amesA.pdf) Sumcheck Arguments and their Applications by Bootle, Chiesa and Sotiraki (https://eprint.iacr.org/2021/333.pdf) Proofs, Arguments, and Zero-Knowledge by Thaler (https://people.cs.georgetown.edu/jthaler/ProofsArgsAndZK.html) Stephen Boyd Papers (https://web.stanford.edu/~boyd/papers.html) *Other links Episode 140: Staking derivatives & DeFi with Alex Evans (and Tarun!) (https://zeroknowledge.fm/140-2/) Episode 206: Distilling DeFi Primitives with Guillermo, Alex and Tarun (https://zeroknowledge.fm/206-2/) Episode 271: Between Two ZK Events with Nico and Guillermo (https://zeroknowledge.fm/271-2/) Episode 282: Error Correcting Codes & Information Theory with Ron Rothblum (https://zeroknowledge.fm/282-2/) Episode 293: Exploring Security of ZK Systems with Nethermind's Michał & Albert (https://zeroknowledge.fm/293-2/) ZK Hack Discord (https://discord.gg/ghsKvMfP) ZK Whiteboard Sessions (https://zkhack.dev/whiteboard/) Applications are now open to attend zkHack Istanbul - Nov 10-12! Apply here: https://www.zkistanbul.com/ (https://www.zkistanbul.com/) Aleo (http://aleo.org/) is a new Layer-1 blockchain that achieves the programmability of Ethereum, the privacy of Zcash, and the scalability of a rollup. As Aleo is gearing up for their mainnet launch in Q4, this is an invitation to be part of a transformational ZK journey. Dive deeper and discover more about Aleo at aleo.org (http://aleo.org/) If you like what we do: * Find all our links here! @ZeroKnowledge | Linktree (https://linktr.ee/zeroknowledge) * Subscribe to our podcast newsletter (https://zeroknowledge.substack.com) * Follow us on Twitter @zeroknowledgefm (https://twitter.com/zeroknowledgefm) * Join us on Telegram (https://zeroknowledge.fm/telegram) * Catch us on YouTube (https://zeroknowledge.fm/)

In episode 86 of The Gradient Podcast, Daniel Bashir speaks to Professor Gil Strang. Professor Strang is one of the world's foremost mathematics educators and a mathematician with contributions to finite element theory, the calculus of variations, wavelet analysis, and linear algebra. He has spent six decades teaching mathematics at MIT, where he was the MathWorks Professor of Mathematics. He was among the first MIT faculty members to publish a course on MIT's OpenCourseware and has since championed both linear algebra education and open courseware.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (02:00) Professor Strang's background and journey into teaching linear algebra* (04:55) Undergrad interests* (07:10) Writing textbooks* (10:20) Prof. Strang's interests in deep learning* (11:00) How Professor Strang thought about teaching early on* (16:20) MIT OpenCourseWare and education accessibility* (19:50) Prof Strang's applied/example-based approach to teaching linear algebra and closing the theory-practice gap* (22:00) Examples!* (27:20) Orthogonality* (29:15) Singular values* (34:40) Professor Strang's favorite topics in linear algebra* (37:55) Pedagogical approaches to deep learning, mathematical ingredients of deep learning's complexity* (42:04) Generalization and double descent in deep learning, powers and limitations* (46:20) Did deep learning have to evolve as it did?* (48:30) Teaching deep learning to younger students* (50:50) How Prof. Strang's approach to teaching linear algebra has evolved over time* (53:00) The Four Fundamental Subspaces* (56:15) Reflections on a career in teaching* (59:49) OutroLinks:* Professor Strang's homepage Get full access to The Gradient at thegradientpub.substack.com/subscribe

Justin and Jason discuss Jason's recent trip to Utah, the molecular biologist who claimed he worked on exo-biospheric organisms, whether "disclosure" is happening soon, Justin's zero-beer hack, Jason and Colby's experience racing high-performance cars at Speed Vegas, the weirdness of The Weekend concert and why it makes Justin worry about the future, the latest with List/Nitro, why Jason doesn't comment on some HN math discussions, what he learned from Colby's Linear Algebra diagnostic, and why he's excited about Colby's game, why taking venture capital will likely kill your company, why Justin loves the game Paper Mario, and Jason's thoughts on Wes Anderson's latest movie Asteroid City. Artwork by https://sonsofcrypto.com. Join our Discord, chat with us and fellow listeners! https://discord.gg/2EbBwdHHx8

Justin and Jason discuss Jason's recent trip to Utah, the molecular biologist who claimed he worked on exo-biospheric organisms, whether "disclosure" is happening soon, Justin's zero-beer hack, Jason and Colby's experience racing high-performance cars at Speed Vegas, the weirdness of The Weekend concert and why it makes Justin worry about the future, the latest with List/Nitro, why Jason doesn't comment on some HN math discussions, what he learned from Colby's Linear Algebra diagnostic, and why he's excited about Colby's game, why taking venture capital will likely kill your company, why Justin loves the game Paper Mario, and Jason's thoughts on Wes Anderson's latest movie Asteroid City. Artwork by https://sonsofcrypto.com. Join our Discord, chat with us and fellow listeners! https://discord.gg/2EbBwdHHx8

Christian has a big announcement! (@ 36 minutes). Chris is done with Linear Algebra, working on Kaggle, and thinking about what type of AI course to make. Christian discovers that FileInbox already has a lot of traffic, but his trial to paid conversion rate is too low.

This is a recap of the top 10 posts on Hacker News on May 11th, 2023.(00:34): The Legend of Zelda: Tears of the Kingdom ReleaseOriginal post: https://news.ycombinator.com/item?id=35912318(01:48): The .zip TLD sucksOriginal post: https://news.ycombinator.com/item?id=35920336(03:11): Implement DNS in a WeekendOriginal post: https://news.ycombinator.com/item?id=35916064(04:30): Toyota: Car location data and videos of 2M customers exposed for ten yearsOriginal post: https://news.ycombinator.com/item?id=35919133(06:00): Linda Yaccarino is the new CEO of TwitterOriginal post: https://news.ycombinator.com/item?id=35917912(07:16): Morris Tanenbaum, inventor of the silicon transistor, has diedOriginal post: https://news.ycombinator.com/item?id=35920261(08:39): Pigz: Parallel gzip for modern multi-processor, multi-core machinesOriginal post: https://news.ycombinator.com/item?id=35914447(10:20): Windmill: Open-source developer platform to turn scripts into workflows and UIsOriginal post: https://news.ycombinator.com/item?id=35920082(11:31): GitHub Copilot Leaked PromptOriginal post: https://news.ycombinator.com/item?id=35924293(12:26): Gilbert Strang's final lecture at MIT: May 15, 11:00amOriginal post: https://news.ycombinator.com/item?id=35921538This is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

I had a blast chatting with David Shimko. We discussed a range of topics from his new book that he is working on that covers a more modern approach to valuation to education and training of new quants. Some interesting topics covered are around the CAPM and options pricing.Simpler Option Pricing (David Shimko):https://youtu.be/uYnYWucILBcDan Stefanica's Primer on Linear Algebra (affiliate link):https://amzn.to/3JIL9pDWebsite:https://www.FancyQuantNation.comSupport:https://ko-fi.com/fancyquantQuant t-shirts, mugs, and hoodies:https://www.teespring.com/stores/fancy-quantConnect with me:https://www.linkedin.com/in/dimitri-biancohttps://twitter.com/DimitriBiancoSupport the show

Train your own AI using this free Lab created by Dr Mike Pound. Big thanks to Brilliant for sponsoring this video! Get started with a free 30 day trial and 20% discount: https://brilliant.org/DavidBombal How do you capitalize on this trend and learn AI? Dr Mike Pound of Computerphile fame shows us practically how to train your own AI. And the great news is that he has shared his Google colab lab with us to you can start learning for free! If you are into cybersecurity or any other tech field, you probably want to learn about AI and ML. They can really help your resume and help you increase the $$$ you earn. Machine Learning / Artificial Intelligence is a fantastic opportunity for you to get a better job. Start learning this amazing technology today and start learning with one of the best! // LAB // Go here to access the lab: https://colab.research.google.com/dri... // Previous Videos // Roadmap to ChatGPT and AI mastery: • Roadmap to ChatGP... I challenged ChatGPT to code and hack: • I challenged Chat... The truth about AI and why you should learn it - Computerphile explains: • The truth about A... // Dr Mike's recommend AI Book // Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP // Dawid's recommend Books // 1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: https://amzn.to/3IrGCHi 2. Pattern Recognition and Machine Learning: https://amzn.to/3IWVm2v 3. Machine Learning: A Probabilistic Perspective: https://amzn.to/3xYFM05 4. Python Machine Learning: https://amzn.to/3y0r08Q 5. Deep Learning: https://amzn.to/3kxSbVu 6. The Elements of Statistical Learning: https://amzn.to/3Iwuuox 7. Linear Algebra and Its Applications: https://amzn.to/3EGwMAs 8. Probability Theory: https://amzn.to/3IrGeZm 9. Calculus: Early Transcendentals: https://amzn.to/3Z3Eugh 10. Discrete Mathematics with Applications: https://amzn.to/3Zpzpyt 11. Mathematics for Machine Learning: https://amzn.to/3m8jp5N 12. A Hands-On Introduction to Data Science: https://amzn.to/3Szob8c 13. Introduction to Algorithms: https://amzn.to/3xXo50K 14. Artificial Intelligence: https://amzn.to/3Z2fqGv // Courses and tutorials // AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for... PyTorch Tutorial From Research to Production: https://www.infoq.com/presentations/p... Scikit-learn Machine Learning in Python: https://scikit-learn.org/stable/ // PyTorch // Github: https://github.com/pytorch Website: https://pytorch.org/ Documentation: https://ai.facebook.com/tools/pytorch/ // Mike SOCIAL // Twitter: https://twitter.com/_mikepound YouTube: / computerphile Website: https://www.nottingham.ac.uk/research... // David SOCIAL // Discord: https://discord.com/invite/usKSyzb Twitter: https://www.twitter.com/davidbombal Instagram: https://www.instagram.com/davidbombal LinkedIn: https://www.linkedin.com/in/davidbombal Facebook: https://www.facebook.com/davidbombal.co TikTok: http://tiktok.com/@davidbombal YouTube: / davidbombal // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! #chatgpt #computerphile #ai

Are you still using loops and lists to process your data in Python? Have you heard of a Python library with optimized data structures and built-in operations that can speed up your data science code? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to share secrets for harnessing linear algebra and NumPy for your projects.

Welcome back to the Digital Doha Podcast, our Qatar-based interview series exploring the cutting-edge of emerging media happening in Doha and the greater Middle East. Hosted by Spencer Striker, PhD Digital Media Professor at Northwestern Qatar, and creator of the award-winning History Adventures digital learning series. For the seventh episode, we continue our exclusive coverage of SIGGRAPH Asia 2022, the world's leading conference on computer graphics and interactive media, which took place in Daegu, South Korea, December 6-9. Celebrating the theme ‘Colorful World', the finest minds redefining the future of cutting-edge technologies gathered to inspire an audience of thousands from around the world. Today's guest, Hyungseok Kim, is Program Chair of SIGGRAPH Asia's XR Track. XR was a juried exhibition within the SIGGRAPH Asia Experience Hall that showcased novel VR, AR, MR prototype systems and/or innovative content with off-the-shelf consumer products and software such as Unity or Unreal Engine. Designers and engineers explained and showcased the concepts behind these novel technologies. Extended Reality (XR) refers to all environments that combine real and virtual elements generated by computer technology. It includes representative forms such as Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR). XR is a superset which includes the entire spectrum from “the complete real” to “the complete virtual” in the reality–virtuality continuum. Hyungseok Kim (“Prof Kim”) is a leading researcher and developer in computer graphics and virtual reality. He researches real-time interaction in virtual environments, multi-resolution modeling of shape and texture, and multi-modal interaction mechanisms. He teaches at Konkuk University on topics including: Computer Graphics, Multimedia Programming, Game Programming, and Linear Algebra. We hope you enjoy our special event coverage of SIGGRAPH Asia 2022, and our conversation with Hyungseok Kim, Program Chair of XR, and one of the world's leading authorities on the vanguard of Extended Reality research. Hosted by: Spencer Striker, PhD Special Guest: Hyungseok Kim NU-Q Research & Production Assistants: Shaikha Alkubaisi; Ayah Mohamedain; Samson Mbogo; Laiba Mubashar; Xingyu Qin; and Tasmia Belal. Emerging Media Research Team: Venus Jin, PhD; Greg Bergida, PhD; John Pavlik, PhD, Justin Gengler, PhD; Farina Amir; and Christopher Fwalanga Artwork: Fernanda Jimenez Music and Sound Effects: Courtesy of Epidemic Music Official website and social media links for the Digital Doha Podcast https://www.qatarpodcasts.com/digital-doha https://www.facebook.com/DigitalDoha https://www.instagram.com/digitaldoha https://twitter.com/doha_digital The Digital Doha Podcast is supported by funding from the Qatar National Research Foundation, Grant ID #: NPRP12S-0227-190165

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Applied Linear Algebra Lecture Series, published by johnswentworth on December 22, 2022 on LessWrong. Over the past couple months, I gave weekly lectures on applied linear algebra. The lectures cover a grab-bag of topics which I've needed to know for my own work, but which typically either aren't covered in courses or are covered only briefly in advanced courses which use them (like e.g. quantum). The series is now complete, and recordings of all the lectures are available here. Be warned: all of the lectures were given with zero review and minimal prep. There are errors. There are poor explanations and too few examples. There are places where I only vaguely gesture at an idea and then say to google it if and when you need it. The flip side is that you will see only things I know off the top of my head - and therefore things which I've found useful enough often enough to remember. Outline of Topics Lecture 1 Prototypical use cases of linear algebra First-order approximation of systems of equations for solving or stability analysis Second-order approximation of a scalar function in many dimensions for optimization or characterizing of peak/bowl shape First-order approximation of a dynamical system near a steady state Principal components of a covariance matrix Lecture 2 Working with efficient representations of large matrices Tricks for jacobian and hessian matrices Prototypical API for implicit matrix representations: scipy's LinearOperator Lecture 3 Suppose we look at a matrix (e.g. using pyplot.matshow()). What patterns are we most likely to see, and what can we do with them? Recognizing sparse & low-rank structure Interpreting sparse & low-rank structure Leveraging sparse & low-rank structure Lecture 4 Matrix calculus, with a focus on stability of eigendecomposition Basics: tensor notation Differentiating eigendecomposition Instability of eigenvectors of (approximately) repeated eigenvalues Lecture 5 Leveraging symmetry Suppose my system is invariant under some permutation (e.g. a PDE with wraparound boundary, or exchangeable variables in a covariance matrix). How can I leverage that to more efficiently find an eigendecomposition (and invert the matrix etc)? What Fourier transforms have to do with symmetry, and how to compute them quickly How to represent rotations/orthogonal matrices Lecture 6 Wedge products: those "dx dy dz" things in integrals How to do coordinate transformations with things like "dx dy", even when embedded in a higher-dimensional space Map between function operations/properties and matrix operations/properties Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Applied Linear Algebra Lecture Series, published by johnswentworth on December 22, 2022 on LessWrong. Over the past couple months, I gave weekly lectures on applied linear algebra. The lectures cover a grab-bag of topics which I've needed to know for my own work, but which typically either aren't covered in courses or are covered only briefly in advanced courses which use them (like e.g. quantum). The series is now complete, and recordings of all the lectures are available here. Be warned: all of the lectures were given with zero review and minimal prep. There are errors. There are poor explanations and too few examples. There are places where I only vaguely gesture at an idea and then say to google it if and when you need it. The flip side is that you will see only things I know off the top of my head - and therefore things which I've found useful enough often enough to remember. Outline of Topics Lecture 1 Prototypical use cases of linear algebra First-order approximation of systems of equations for solving or stability analysis Second-order approximation of a scalar function in many dimensions for optimization or characterizing of peak/bowl shape First-order approximation of a dynamical system near a steady state Principal components of a covariance matrix Lecture 2 Working with efficient representations of large matrices Tricks for jacobian and hessian matrices Prototypical API for implicit matrix representations: scipy's LinearOperator Lecture 3 Suppose we look at a matrix (e.g. using pyplot.matshow()). What patterns are we most likely to see, and what can we do with them? Recognizing sparse & low-rank structure Interpreting sparse & low-rank structure Leveraging sparse & low-rank structure Lecture 4 Matrix calculus, with a focus on stability of eigendecomposition Basics: tensor notation Differentiating eigendecomposition Instability of eigenvectors of (approximately) repeated eigenvalues Lecture 5 Leveraging symmetry Suppose my system is invariant under some permutation (e.g. a PDE with wraparound boundary, or exchangeable variables in a covariance matrix). How can I leverage that to more efficiently find an eigendecomposition (and invert the matrix etc)? What Fourier transforms have to do with symmetry, and how to compute them quickly How to represent rotations/orthogonal matrices Lecture 6 Wedge products: those "dx dy dz" things in integrals How to do coordinate transformations with things like "dx dy", even when embedded in a higher-dimensional space Map between function operations/properties and matrix operations/properties Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Episode: 2557 Linear algebra, the mathematics behind Google's ranking algorithm.  Today, let's talk about how Google ranks your search results.

'Artificial Intelligence: A Modern Approach', 4th ed., pages 1023–1029. Subscribe at: https://paid.retraice.com Details: day 4 — math; A1 — problem and algorithm analysis; A2 — line equation probing; A3 — quantifying 'probably'. Complete notes and video at: https://www.retraice.com/segments/re60 Air date: Thursday, 24th Nov. 2022, 11 : 00 PM Eastern/US. 0:00:00 day 4 — math; 0:05:59 A1 — problem and algorithm analysis; 0:14:15 A2 — line equation probing; 0:22:00 A3 — quantifying 'probably'. Copyright: 2022 Retraice, Inc. https://retraice.com

Episode: 2544 How humans and computers recognize faces.  Today, UH math professor Krešo Josić recognizes your face.

Episode: 2514 How Netflix uses linear algebra to determine what movies you will like best.  Today, UH math professor Krešo Josić talks about math and your movie choice.

David on Twitter - https://twitter.com/LittleFunnyGeek David's Website - http://davidpham87.github.io Linear Algebra - https://en.wikipedia.org/wiki/Linear_algebra Google Colab - https://colab.research.google.com Paperspace - https://www.paperspace.com Copula - https://en.wikipedia.org/wiki/Copula_(probability_theory) Criterium - https://github.com/hugoduncan/criterium/ Matrix - https://github.com/mikera/core.matrix Video Courses: https://clojure.stream https://learnpedestal.com https://learndatomic.com https://learnreitit.com https://learnreframe.com https://learnreagent.com https://www.jacekschae.com

Gilbert Strang has made many contributions to mathematics education, including publishing seven mathematics textbooks and one monograph. Strang is the MathWorks Professor of Mathematics at the Massachusetts Institute of Technology. He teaches Introduction to Linear Algebra and Computational Science and Engineering and his lectures are freely available through Massachusetts Institute of Technology OpenCourseWare. Professor Strang is well-known for his unique and somewhat offbeat presentation style that many find to be both entertaining and highly informative. B. Ph. Professor of Mathematics, Massachusetts Institute of Technology (since 1962) Honorary Fellow, Balliol College, Oxford President, Society for Industrial and Applied Mathematics (1999, 2000) Chair, United States. National Committee on Mathematics (2003–2004) Chair, National Science Foundation (National Science Foundation) Advisory Panel on Mathematics Board Member, International Council for Industrial and Applied Mathematics (ICIAM) Abel Prize Committee (2003–2005). —————————————————————————————

It's episode 185 of the MeFi Monthly Podcast, with Jessamyn and I talking for a good chunk up front about the whole process over the last couple months of figuring out transferring ownership of the site from me to her. We also talk about, like, good stuff from the site for most of it.Helpful LinksPodcast FeedSubscribe with iTunesDirect mp3 downloadOnce again I am gloriously doing almost no work in causing this post to be here; thanks again to eotvos who has once again done all the actually fiddly bits in turning Jess and I rambling on mic into an actual mix down and pile of text as seen below. Projects - I made Some Tools by bondcliff. - The Daily Brief -- News as Information by jkrobin. - ... a look back at your Amazon shopping history by ph00dz. - Every .horse domain by Shepherd. - Dark Patterns Now Available on Android and iOS by cosmic owl. Metafilter - Mechanical Watch by Devils Rancher. - blank blank in the blank of blankety blank, blank blank? by Ten Cold Hot Dogs. - The "FU" is how you answer the phone when the man tries to bring ya down by not_on_display. - The Uselessness of Phenylephrine by brainwane. - Something Went Very Wrong by cavenet. - Things that Make White People Uncomfortable by box. - The Mefi-wiki page about Givewell. - Now you know your A-B-Trees by secretdark. - You're welcome, Matt, by zenon. Ask Metafilter - Everything Everywhere All At When? (streaming) by fleecy socks. - How should I learn Linear Algebra? by cortex. - Expressing a line figure as a set of triangles by Tell Me No Lies. - Advice on Art Appraisals, by Saxon Kane. - Navigating complicated grief for alcoholic father by showeringsuns. - What happened to the squatters? by wesleyac. - Is saying "I'm proud of you" patronizing? by Dressed to Kill. - Is it wrong to use these antique postcards as postcards? by The corpse in the library. Metatalk - Paperwork & Bodywork: short virtual anti-procrastination calls by brainwane. - [MeFi Site Update] May 25th by loup and staff. - MetaFilter: A Utopia of Rules? by General Malaise. - A MetaFilter User Survey by curious nu and the transition team. Music clips - Bigass Pizza Blues by CarrotAdventure - Look At Me by transitional procedures. - One Month Dragon by srednivashtar - For Each One To Discover (AO) by q*ben. - End Credits by CarrotAventure.

Mark Hoemmen joins Rob and Jason. They first talk about an debugging improvements in VS Code and C++20/23 features going into MSVC. Then they talk to Mark Hoemmen about his past work on linear algebra libraries Tpetra and Kokkos, and current efforts to get linear algebra into the standard. News What's new for C++ debugging in VS Code Conformance should mean something - fputc, and freestanding MSVC C++20/23 Update Links Tpetra parallel linear algebra P1417R0: Historical lessons for C++ linear algebra library standardization P1673R7: A free function linear algebra interface based on the BLAS P1674R1: Evolving a Standard C++ Linear Algebra Library from the BLAS Patreon CppCast Patreon  

In this episode, Nicole shares some new books she's purchased for the library's nonfiction sections, and they welcome Melissa, Circulation Assistant, to the show. The resources discussed in this episode are discussed below: The Mean Girls: A Bunch of Bullies by Atiyah C. Henley; Find Your Unicorn Space: Reclaim Your Creative Life in a Too-Busy World by Eve Rodsky; What Is Black Lives Matter? by Lakita Wilson; What Were the Negro Leagues? by Varian Johnson; What Is Congress? by Jill Abramson; What Was the Bombing of Hiroshima? by Jess Brallier; What Was the Holocaust? by Gail Herman; What is Rock and Roll? by Jim O'Connor; What Are the Winter Olympics by Gail Herman; Healing: Our Path from Mental Illness to Mental Health by Thomas Insel; Pirate Queens: Dauntless Women who Dared to Rule the High Seas by Leigh Lewis; The Wisteria Society of Lady Scoundrels by India Holton; Her Name is Knight by Yasmin Angoe; The Once and Future Witches by Alix E. Harrow; The Ten Thousand Doors of January by Alix E. Harrow; Crook Manifesto by Colson Whitehead; The Underground Railroad by Colson Whitehead; Harlem Shuffle by Colson Whitehead; The Nickel Boys by Colson Whitehead; Sag Harbor by Colson Whitehead; Zone One by Colson Whitehead; Read Dangerously: The Subversive Power of Literature in Troubled Times by Azar Nafisi; The Princess Bride Cookbook by Jen Fujikawa; The Manga Guide to Linear Algebra by Shin Takahashi and Iroha Inoue; Bridgerton Netflix show; Strike HBO show

In this first of two episodes, Patrick and Greg lay the foundations of matrix algebra, mathematically and geometrically, and start connecting these important underlying ideas to statistics. Along the way, they also mention STDs, the 110 to the 10 to the 405, mystics, skin bags of water, vector Victor, Tac flashlights on misty nights, remembering mnemonics, Stephen Hawking, Bilbo Baggins, and Greg's sultry voice. 

The resources discussed in this episode are listed below: Right Beside You by Mary Monroe; Dreaming Anastasia, a novel of love, magic, and the power of dreams by Joy Preble; The Family Romanov: Murder, Rebellion, and the Fall of Imperial Russia by Candace Fleming; The Manga Guide to Biochemistry by Masaharu Takemura and Kikuyaro; The Manga Guide to Calculus by Hiroyuki Kojima and Shin Togami; The Manga Guide to Statistics by Shin Takahashi; The Manga Guide to Linear Algebra by Shin Takahashi; The Manga Guide to Physics by Hideo Nitta and Keita Takatsu; This Is Your Brain on Stereotypes: How Science Is Tackling Unconscious Bias by Tanya Lloyd Kyi and Drew Shannon; How to Draw Deluxe Edition (Pokémon) by Maria S. Barbo and Tracey West, illustrated by Ron Zalme; The Life of Frederick Douglass: A Graphic Narrative of a Slave's Journey from Bondage to Freedom by David F. Walker, illustrated by Damon Smyth and Marissa Louise; Alexander Hamilton: The Graphic History of an American Founding Father by Jonathan Hennessey, illustrated by Justin Greenwood; Murder Book: A Graphic Memoir of a True Crime Obsession by Hilary Fitzgerald Campbell; On Tyranny Graphic Edition: Twenty Lessons from the Twentieth Century by Timothy Snyder and Nora Krug; On Tyranny Graphic Edition: Twenty Lessons from the Twentieth Century by Timothy Snyder and Nora Krug;

Catch Dave on Episode 006 of Greater Than Code! Getting Technology Into the Hands of Children with David Bock (https://www.greaterthancode.com/getting-technology-into-the-hands-of-children) 02:10 - Dave's Superpower: Ability to Reevaluate and Drop Ideas – Onto The Next! * Star Trek: The Next Generation (https://en.wikipedia.org/wiki/Star_Trek:_The_Next_Generation) * Impostor Syndrome (https://en.wikipedia.org/wiki/Impostor_syndrome) 07:10 - The Acceptance of Ruby; Using Ruby as a Teaching Language * Teaching Ruby Makes Approaching Computer Science Approachable * Intro To Programming Skill Tree.md (https://gist.github.com/caseywatts/93cba34cd882a05b3107) * Computational Thinking (https://en.wikipedia.org/wiki/Computational_thinking) * Object-Oriented Programming (https://en.wikipedia.org/wiki/Object-oriented_programming) * Functional Programming (https://en.wikipedia.org/wiki/Functional_programming#:~:text=In%20computer%20science%2C%20functional%20programming,by%20applying%20and%20composing%20functions.&text=When%20a%20pure%20function%20is,state%20or%20other%20side%20effects.) * Primer on Python Decorators (https://realpython.com/primer-on-python-decorators/) 18:01 - Mobile Development * Accessibility * FingerWorks (https://en.wikipedia.org/wiki/FingerWorks) * Teaching Performance; Linear Algebra (https://en.wikipedia.org/wiki/Linear_algebra) * Star 26 Math Puzzle (https://www.puzzlemaster.ca/browse/wood/otherwood/12292-star-26-math-puzzle) * Aristotle Number Puzzle (https://www.amazon.com/s?k=aristottles+number+puzzle&ref=nb_sb_noss_2) 24:10 - Teaching Remotely * WatchDOG Dads (https://www.pickerington.k12.oh.us/violet-elementary/watch-dog-dads/) * Cameras On/Off * % of Women Went Up / Gatekeeping and Gender Bias * Grace Hopper (https://en.wikipedia.org/wiki/Grace_Hopper) 34:25 - Computer Science Education Week (https://www.csedweek.org/) + Teaching/Volunteering * Hour of Code (https://hourofcode.com/) * Code.org (https://code.org/) * Scratch (https://scratch.mit.edu/) “Computers aren't smart. They're just dumb really, really fast.” Understanding the Pareto Principle (The 80/20 Rule) (https://betterexplained.com/articles/understanding-the-pareto-principle-the-8020-rule/) Zero: The Biography of a Dangerous Idea (https://www.amazon.com/Zero-Biography-Dangerous-Charles-Seife/dp/0140296476) Plimpton 322 (https://en.wikipedia.org/wiki/Plimpton_322) 56:39 - Handling Time Management and Energy * Ted Lasso (https://en.wikipedia.org/wiki/Ted_Lasso) * Getting Positive by Looking at the Negative Reflections: Casey: Motivating students to learn algorithmic efficiency. Feeling the problem. Mae: Becoming more involved in the community. Chelsea: What are people in the tech world ready for? Dave: How much talking about computer science education is invigorating and revitalizing. Seeing problems through beginners' eyes. This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps, LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode) To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well. Special Guest: Dave Bock.

MEP EP#297: Modern College Education with Derek FronekBefore we begin this episode I have a quick announcement. If you are currently enrolled in college we would love to chat with you. We have some ideas for future podcast content that you could perhaps help us with. Also, we would love to get to know our listeners more. Please send a hello email to podcast@macrofab.com. Derek Fronek Third year EE Co-op student at Purdue University Last on the podcast episode #146 where he spoke about his time with TechHOUNDS Modern EE Education Stephen - Graduated from Texas A&M with a EE degree in 2009 128 hours required Parker - Graduated from University of Texas with a ECE degree in 2011 Derek - Will graduate in 2024 Derek's Current class load Freshman High school credits that transfer help a lot First year Engineering Purdue's way of saying “you need to do basic math and science first” General Chemistry EPICS (project based design class) Calc 1-2 Physics Mechanics/E&M CS-159 Intro to C Covid Happens and we go remote right after spring break Summer Online summer classes can be nice but its a 16 weeks class in 8 weeks ECE 20001 Fundamentals 1 (Basic Circuit Theory/ Mosfets) ECE 20007 Fundementals 1 Lab Analog Discovery 2 is the main test and measurement tool now Calc 3 Sophomore First Co-op term at Rheem Visiting campus when you have no class responsibilities is fun First real “Covid Classes” Purdue managed to stay “in person” for the whole time Classes were still mostly online but students could be on campus and some labs were still in person Exams were mostly online as well ANTH 21000 Technology and Culture More interesting than i thought it would be, gave some perspective on how design choices have social and societal implications ECE 20002 Fundementals 2 (Amplifiers, Op Amps, AC Circuit Analysis) ECE 20008 Fundementals 2 Lab ECE 264 Advanced C MA 266 Diff eq Junior Back to in person classes this fall ECE 20875 Python for Data Science ECE 270 Digital System Design Auto lab system is really cool HIST 351 MA 265 Linear Algebra

Dr. Brian Mulholland is an Assistant Professor of the Practice in the Mathematics Department at Notre Dame and the Director of the ASCEND program, which is the summer online program for the incoming first years. He works primarily in digital resource development and mathematical pedagogy. In the past few years, he helped create both the Summer Online Calculus III and Introduction to Linear Algebra and Differential Equations courses. He frequently implements digital materials and alternative teaching practices and plans to further research the impact of these non-traditional teaching methodologies to enhance student learning. Special Guest: Brian Mulholland.

D Creations Mathematics Physics Linear Algebra Vector Spaces --- Send in a voice message: https://anchor.fm/d0531/message

"Mathematics. Oh?" Yeah... math. Remember? That subject from back in school and/or uni that you loved or hated or maybe just got along with ok-ish. And mathematics for developers - what is this? This episode is actually Kai's fault. He clearly likes mathematics and even studied it full time at university.We start as usual by going through a few things we've found online and talk about what we've been doing over the last few days and weeks. There's some exciting stuff in there, but you'll need to listen to the episode to get all these details.We launch into the actual topic by talking about how we feel about mathematics and how we use it in our lives. That varies from day-to-day arithmetic up to work usage to solve some types of problems.We talk about how you learn mathematics in school and how that progresses into university mathematics. One key component of enjoying mathematics and developing an interest in the topic seems to be teachers and their ways of knowledge transfer.Learning mathematics at university seems to be a very different experience: Lectures on the blackboard, hard to follow and understand and totally geared towards an academic career and not towards practical applications.But how do you learn math in a better way - or how do you get back into mathematics after some years of not using it? And how does that apply to development work?One way of looking at mathematics can be by trying to identify building blocks after you have picked a practical application. Let's say you want to work on some piece of code to animate a 2- or 3D object.Sure, you can just use a library. But if you want to understand the math behind it, you'd have to learn about rotation matrices. That leads you into having to understand a bit more about matrices and trigonometrical functions. Matrices are Linear Algebra, then there's some aspects of Geometry involved and functions are covered in Calculus. The interesting thing is that a lot of practical applications reduce to a limited amount of these building blocks. They act as reusable sets of knowledge that will help you to understand a variety of different topics across mathematics and computer science: Data Analysis, Machine Learning, Animations and more.We have some resources for you to help you get going. Video-based learning:Khan Academy Domain of ScienceMathematics for Machine LearningData Mining with Weka Tools and online communities:GeoGebra CommunityWolfram AlphaPaul Dawkins' Online NotesOctaveRTeX/LaTeX Books:Jeremy Kun - A programmer’s introduction to mathematicsJohn Stillwell - From Euclid to Goedel

Episode: 2557 Linear algebra, the mathematics behind Google's ranking algorithm.  Today, let’s talk about how Google ranks your search results.

The main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled “Statistical Machine Learning: A unified framework.”  Chapter 4 is titled “Linear Algebra for Machine Learning. Many important and widely used machine learning algorithms may be interpreted as linear machines and this chapter shows how to use linear algebra to analyze and design such machines. In addition, these same techniques are fundamentally important for the development of techniques for the analysis and design of nonlinear machines.  This podcast provides a brief overview of Linear Algebra for Machine Learning for the general public as well as information for students and instructors regarding the contents of Chapter 4 of Statistical Machine Learning. For more details, check out: www.statisticalmachinelearning.com

Tune in to enjoy host Julie Mochan talk in depth to Nick Scalzo, CEO of RiskPro's parent company about the origin and development of this RegTech solution. Nick then shares quite a bit about his history growing up in Southern California,and the Orange County punk music scene. There are lots of shout outs to many people that have influenced and helped Nick all along the way to his current successes that he shares with his Co-CEO Megan Meade! Click for More About RiskPro Office: 949-259-6928 2077 West Coast Highway, Suite A Newport Beach, CA 92663 solutions@riskproadvisor.com Talk to Julie Here or email me: JulieM@tpfg.com Mentioned in this Podcast: Josh Emanuel, Chief Investment Officer for Wilshire Funds Management In his role as CIO, Mr. Emanuel leads the investment activities of Wilshire Funds Management, including asset allocation, manager research, portfolio management, and investment research. Mr. Emanuel also chairs the Wilshire Funds Management Investment Committee. Dr. Alfonso Agnew, Professor and Chair, Mathematics, California State University Fullerton Courses: Vector and Tensor Analysis, General Relativity, Partial Differential Equations, Linear Algebra and Ordinary Differential Equations Research Areas: Mathematical issues in classical and quantum gravity -- General Relativity Theory and gravitational radiation, curved space quantum field theory, twistor theory, Non-Hausdorff spaces This recording has been prepared and made available by RiskPro® to be used for information purposes only. RiskPro® is an investment risk profiling and portfolio construction software as a service platform developed by ProTools, LLC (“ProTools”). The information contained herein, including any expressions of opinion, has been obtained from or is based on sources believed to be reliable but its accuracy or completeness is not guaranteed and is subject to change without notice. Any expressions of opinions reflect the views of the speakers and are not necessarily those of ProTools or its affiliates. ProTools does not provide investment, tax or legal advice. Investors should consult their financial, tax or legal professionals before investing. Any third parties mentioned in the podcast have no affiliation with the Pacific Financial Group, Inc. or ProTools, LLC.

This weeks recap

Pada episode kali ini kami membahas tentang kuliah Aljabar Linear dan alasan dibalik nama podcast kami.

This episode covers the concept of linear algebra important for Quantum Mechanics. (P.S. This is not the complete course as some of the things may not be covered completely)

I would say using teaching as a way to get better at something is a really good approach. And I would say that if you don't have a lot of knowledge, you shouldn't think you can't teach. I'm very much a proponent of ... being able to take ideas you have and using them to kind of affect your surroundings. And I think that teaching is a great way to do that. Hosts Karin Thorne and Kelly Corey chat with Ryan Gaus about choosing to work for a software company instead of going to college, his experience teaching at Careers in Code, and his journey as a self-taught developer. Ryan also shouts out some past attendees of the OpenHack Meetup and talks to us about a cool project he's working on with his mom. This episode is part V of our series: Catching Up with Careers in Code! We chat with founders, instructors/TAs, and students of the first CiC cohort about their experience and where they'd like to see the program go in the future. Connect with Ryan rgaus.net (https://rgaus.net/) | LinkedIn (https://www.linkedin.com/in/ryan-gaus-08068617b/) | Instagram (https://instagram.com/rgausgaus) | Twitter (https://twitter.com/rgausnet) Episode References Strandbeest (https://www.strandbeest.com/) | Linear Algebra course on MITOpenCourseware (https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) Special Announcement As of April 7, 2020, Salt City Code will be switching from a weekly to a biweekly episode format. Karin and Kelly feel this will be the best way to balance our personal and professional lives while continuing to run the podcast. Thank you all so much for listening and sharing your tech journeys with us! Music This episode features "Brain Power" by Mela (https://freemusicarchive.org/music/Mela/Mela_two) from the album Mela two. Follow Karin kethorne.com (http://www.kethorne.com/) | Twitter (https://twitter.com/kaythorne) | Instagram (https://www.instagram.com/karin_thorne/) | E-mail (mailto:contact@kethorne.com) JSWebb Development, LLC jswebbdevelopment.com (https://jswebbdevelopment.com/) | Twitter (https://twitter.com/JSWebb_Dev) | Instagram (https://www.instagram.com/jswebbdev/) | E-mail (mailto:jswebbdevelopment@gmail.com) Follow Kelly kell.dev (https://kell.dev/) | Twitter (https://twitter.com/kellytoearth) | Instagram (https://www.instagram.com/kellytoearth/) | E-mail (mailto:hello@kell.dev) Follow Salt City Code Twitter (https://twitter.com/saltcitycode) | Instagram (https://www.instagram.com/saltcitycode/) | E-mail (mailto:saltcitycode@gmail.com) --- Special Guest: Ryan Gaus.

Kelly St. Onge is an Artistically Science-Minded Mathematician with dreams of being an Astronaut. Attending college courses in Bellingham, Longview, Vancouver, and Portland - Kelly has thus far completed classes in Engineering Physics, Trigonometry, Pre-Calculus through Calc-4, Linear Algebra, Differential Equations, Discrete Math, Java, Data Structure, C++ Programming, and many more (that I think he casually breezed over). Kelly's interests are vast, from Rick and Morty to Star Wars, Travis Scott to Hall N Oates, from people to robots and football to snowboarding - he's passionate about learning, living life with a smile, and deep dirty heavy filthy, bass. We begin with a scaffolding of Mathematics, Science, Quantum Physics, Quantum Computing, and Artificial Intelligence. We touch on lots of things math and science: Layers of abstraction, bits, programming, compiling, machine code, particles, superposition, prime numbers, infinite numbers, induction and some current progressions surrounding A.I. - and we swing by Schrodingers Cat, A-Ha Moments, De-bugging YO' SELF, The Mandolorian, Malicious Compliance, The Singularity, Kung-Fu Panda, and even Illegal Numbers. Kelly and I together try to use silly analogies, broad terminologies, terrible accents, a couple of beers and a long-lasting friendship to hopefully open up new possibilities and understandings for listeners. Science, Math, Music... it's all Art. It's all language for the taking. Not blocked off for any certain types or caged by any One Way-sayers. Being open, friendly, and interested can bring you great knowledge and companionship. When Kelly and I first met I had recently graduated high school and fell in love with electronic music - we bonded over Bassnectar's Divergent Spectrum album and soon after he actually taught me the basics of djing and let me take home his Dj Controller to practice. Who knew that his kindness and our mutual love for music would catapult a friendship through a decade of adventures? Cheers to many more, Sir K3lls. What program are you running? is it time to upgrade your mental software? Can you De-bug your own system? When you learn processes pertaining to new knowledge, how can you return those same processes inward towards old stories? Create the mental structure that is sensible to you and start listening to, or searching for - the wisdom that surrounds us all. Be well.

Mathematics Professor Gilbert Strang is one of MIT’s most revered instructors; his courses, especially the perennially popular linear algebra course 18.06, have received millions of visits on OpenCourseWare, and his lecture videos have won him a devoted following on YouTube as well. (A sample YouTube comment on one of his lectures: “This is not lecture, this is art.”) A few years ago, Professor Strang began teaching a new course (18.065) focusing on the application of mathematical matrices to deep learning and AI. This new course is very unlike a typical undergraduate math course. For one thing, there’s no final exam—in fact, there are no exams at all! Instead, Professor Strang asks each student to spend the semester developing a project that applies the techniques they’re studying to some topic or problem they personally find interesting. In this episode, we hear from Professor Strang about his efforts to humanize math teaching, the value of thinking through problems in real time during lectures—even if it means getting stuck and having to backtrack!—and the importance of staying continually conscious of your students.    Relevant Resources:MIT OpenCourseWareThe OCW Educator Portal 18.065 on OCW18.06 on OCW18.06 Scholar on OCWProfessor Strang’s faculty pageProfile of Professor StrangMusic in this episode by Blue Dot SessionsConnect with UsIf you have a suggestion for a new episode or have used OCW to change your life or those of others, tell us your story. We’d love to hear from you! On our siteOn FacebookOn TwitterOn InstagramStay CurrentSubscribe to the free monthly "MIT OpenCourseWare Update" e-newsletter.

Our latest student lecture features the first lecture in the second term 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 lectures are followed by tutorials where pairs of students spend an hour with their tutor to go through the lectures and accompanying work sheets. An overview of the course and the relevant materials is available here: https://courses.maths.ox.ac.uk/node/43829

Our latest student lecture features the first lecture in the second term 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 lectures are followed by tutorials where pairs of students spend an hour with their tutor to go through the lectures and accompanying work sheets. An overview of the course and the relevant materials is available here: https://courses.maths.ox.ac.uk/node/43829

This week we talk about the dreaded stereonet and why it's useful and somewhat... odd. Great Stereonet Video Series On Teaching Stereonets Visible Geology Fun Paper Friday What's the best way to make a uniform crepe or pancake? Find out with just a bit of math! Boujo, E., and M. Sellier. "Pancake making and surface coating: optimal control of a gravity-driven liquid film." Physical Review Fluids 4.6 (2019): 064802. Contact us: Show Support us on Patreon! www.dontpanicgeocast.com SWUNG Slack @dontpanicgeo show@dontpanicgeocast.com John Leeman www.johnrleeman.com @geo_leeman Shannon Dulin @ShannonDulin

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. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts or support it on Patreon. This episode

  Far too often, we evaluate math ability in high schoolers solely on the basis of grades and level of math learned. A more accurate assessment of a student’s potential on challenging math tasks--including those posed on tests like the SAT and ACT--should consider mathematical maturity.  Amy and Mike invited author and test prep professional Dr. Steve Warner to define what this means and explain the link between mathematical maturity and test success.  What are five things you will learn in this episode? What is mathematical maturity? How can you determine your "level" of mathematical maturity? Can mathematical maturity be improved? How does mathematical maturity relate to standardized test scores? What steps can students seeking higher levels of mathematical maturity take? MEET OUR GUEST Dr. Steve Warner, a New York native, earned his Ph.D. at Rutgers University in Pure Mathematics in May 2001. After Rutgers, Dr. Warner joined the Penn State Mathematics Department as an Assistant Professor and in September 2002, he returned to New York to accept an Assistant Professor position at Hofstra University. By September 2007, Dr. Warner had received tenure and was promoted to Associate Professor. He has taught undergraduate and graduate courses in Precalculus, Calculus, Linear Algebra, Differential Equations, Mathematical Logic, Set Theory, and Abstract Algebra. From 2003 – 2008, Dr. Warner participated in a five-year NSF grant, “The MSTP Project,” to study and improve mathematics and science curriculum in poorly performing junior high schools. He also published several articles in scholarly journals, specifically on Mathematical Logic. Dr. Warner has nearly two decades of experience in general math tutoring and tutoring for standardized tests such as the SAT, ACT, GRE, GMAT, and AP Calculus exams. He has tutored students both individually and in group settings. In February 2010 Dr. Warner released his first SAT prep book “The 32 Most Effective SAT Math Strategies,” and in 2012 founded Get 800 Test Prep. Since then Dr. Warner has written books for the SAT, ACT, SAT Math Subject Tests, AP Calculus exams, and GRE. In 2018 Dr. Warner released his first pure math book called “Pure Mathematics for Beginners.” Since then he has released several more books, each one addressing a specific subject in pure mathematics. Dr. Steve Warner can be reached at steve@SATPrepGet800.com LINKS Gaining Mathematical Maturity Dr. Warner’s extensive catalog of math prep books ABOUT THIS PODCAST Tests and the Rest is THE college admissions industry podcast. Explore all of our episodes on the show page.

Roger Hart‘s The Chinese Roots of Linear Algebra (Johns Hopkins University Press, 2011) is the first book-length study of linear algebra in imperial China, and is based on an astounding combination of erudition and expertise in both Chinese history and the practice and history of linear algebra. Alternating among an... Learn more about your ad choices. Visit megaphone.fm/adchoices