--- title: Best Math posts of 2024 | daily.dev description: The most upvoted Math posts of 2024, curated by the daily.dev community. canonical: https://daily.dev/tags/math/best-of/2024 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/math/best-of/2024 og:type: website og:site_name: daily.dev og:title: Best Math posts of 2024 | daily.dev og:description: The most upvoted Math posts of 2024, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Math — 2024 1. 1 [](/posts/you-are-not-dumb-you-just-lack-the-prerequisites-l63uu7tjw "You Are NOT Dumb, You Just Lack the Prerequisites") Article Hacker News · 2y You Are NOT Dumb, You Just Lack the Prerequisites Struggling to learn complex subjects like math often stems from missing prerequisite knowledge, not a lack of capability. Revisiting and mastering foundational concepts can help build the necessary groundwork for understanding more advanced material. It's important to take a step back and re-learn the basics using effective methods and consistent practice. 2K 111 2. 2 [](/posts/5-free-courses-to-master-math-for-data-science-x2k0uoknl "5 Free Courses to Master Math for Data Science") Article KDnuggets · 2y 5 Free Courses to Master Math for Data Science Learn math for data science with these free courses on topics such as calculus, linear algebra, probability and statistics, and optimization. 154 9 3. 3 [](/posts/big-o-explained-with-a-deck-of-cards-uihyduggq "Big O explained with a deck of cards") Video Fireship · 2y Big O explained with a deck of cards 145 3 4. 4 [](/posts/the-fundamental-mathematics-of-machine-learning-nio70zhoc "The Fundamental Mathematics of Machine Learning") Article Towards AI · 2y The Fundamental Mathematics of Machine Learning This blog explores the core mathematical concepts essential for understanding and building machine learning models. It dives deep into linear algebra and calculus, highlighting their importance in model training and optimization. The post provides practical applications, case studies, and step-by-step examples to enhance your grasp of these foundational principles. 138 5. 5 [](/posts/degenerative-ai-the-recent-failures-of-artificial-intelligence-tech-271r4dnso "Degenerative AI… The recent failures of "artificial intelligence" tech") Video Fireship · 2y Degenerative AI… The recent failures of "artificial intelligence" tech Recent failures of artificial intelligence technology are discussed, including the instability of AI companies and the misleading nature of AI models. 136 6 6. 6 [](/posts/scala-in-100-seconds-bdiggluwn "Scala in 100 Seconds") Video Fireship · 2y Scala in 100 Seconds 126 2 7. 7 [](/posts/deep-learning-course-math-and-applications-l3fbpgqss "Deep Learning Course – Math and Applications") Article freeCodeCamp · 2y Deep Learning Course – Math and Applications Learn the math behind deep learning with a 14-hour course on the freeCodeCamp YouTube channel. Developed by Ayush Singh, the course covers fundamental concepts, deep learning techniques, mathematical insights, and practical applications. Topics include vectors, matrices, linear algebra, calculus, machine learning, and neural networks. 118 1 8. 8 [](/posts/practical-guide-to-linear-algebra-in-data-science-and-ai-uief7yrio "Practical Guide to Linear Algebra in Data Science and AI") Article freeCodeCamp · 2y Practical Guide to Linear Algebra in Data Science and AI Linear algebra is a practical tool that can be used to solve real-world problems in data science and AI. It is applied across various industries, and understanding its core concepts is essential for working with machine learning, deep learning, computer vision, and generative AI. A linear algebra roadmap for 2024 is provided to guide your learning journey, and there are numerous resources available to help you master linear algebra. 98 1 9. 9 [](/posts/linear-algebra-concepts-every-data-scientist-should-know-w5xt70kis "Linear Algebra Concepts Every Data Scientist Should Know") Article Medium · 2y Linear Algebra Concepts Every Data Scientist Should Know Linear algebra is fundamental in transforming theoretical data science models into practical solutions. It is crucial for data representation, dimensionality reduction, optimization, feature engineering, and similarity measures. Concepts such as vectors, vector spaces, matrices, and operations like dot products and matrix multiplication are key foundational topics. Understanding the basis, rank, determinants, eigenvectors, and eigenvalues are vital for advanced applications in data science and machine learning. 82 10. 10 [](/posts/25-most-important-mathematical-definitions-in-data-science-d87s83jjy "25 Most Important Mathematical Definitions in Data Science") Article Daily Dose of Data Science | Avi Chawla | Substack · 2y 25 Most Important Mathematical Definitions in Data Science The importance of mathematical knowledge in data science and machine learning, a list of important mathematical formulations used in data science and statistics, and the use of mean squared error (MSE) in machine learning. 82 11. 11 [](/posts/jax-in-100-seconds-owxesepqj "JAX in 100 Seconds") Video Fireship · 2y JAX in 100 Seconds Jax is an accelerated linear algebra library that allows for high-performance array computing and automatic differentiation. It enforces constraints like immutable arrays and pure functions. Jax can run on accelerated hardware like GPUs and TPUs. 76 12. 12 [](/posts/5-free-mit-courses-to-learn-math-for-data-science-0cszf8dzc "5 Free MIT Courses to Learn Math for Data Science") Article KDnuggets · 2y 5 Free MIT Courses to Learn Math for Data Science Learn math for data science with free courses from MIT, covering topics such as linear algebra, calculus, statistics, and probability. 69 4 13. 13 [](/posts/physics-vs-mathematics-vs-programming--ypqleilo1 "Physics VS Mathematics VS programming 😎") Article Watercooler · 2y Physics VS Mathematics VS programming 😎 A comparison between physics, mathematics, and programming, exploring their differences and the skills required for each. 64 2 14. 14 [](/posts/srinivasa-ramanujan-was-a-genius-math-is-still-catching-up--2gxgxmfkm "Srinivasa Ramanujan Was a Genius. Math Is Still Catching Up.") Article Hacker News · 2y Srinivasa Ramanujan Was a Genius. Math Is Still Catching Up. Srinivasa Ramanujan, a self-taught Indian mathematician, made groundbreaking contributions to mathematics, including partition identities that are still revealing new insights today. Mathematicians like Hussein Mourtada are uncovering deep connections between Ramanujan's work and modern mathematical fields such as algebraic geometry, knot theory, and prime number detection. Ramanujan's results often appeared without proof and are considered divinely inspired by his contemporaries and successors, continuing to impact various branches of mathematics more than a century later. 45 1 15. 15 [](/posts/introduction-to-computational-graphs-jjgoj4lnq "Introduction to computational graphs") Article The Palindrome · 2y Introduction to computational graphs Introduction to computational graphs and their importance in managing the complexity of large models like deep neural networks. Learn how computational graphs make machine learning computationally feasible. 43 2 16. 16 [](/posts/linear-algebra-crash-course---mathematics-for-machine-learning-and-generative-ai-yhbmvz33x "Linear Algebra Crash Course - Mathematics for Machine Learning and Generative AI") Article freeCodeCamp · 2y Linear Algebra Crash Course - Mathematics for Machine Learning and Generative AI Learn linear algebra foundations for data science and AI in a 6-hour crash course on the freeCodeCamp.org YouTube channel. 42 17. 17 [](/posts/does-not-compute--goaxncynx "Does not compute!") Article Dev Leader · 3y Does not compute! A post about something related to math that may be humorous and relevant for the weekend. 41 7 18. 18 [](/posts/5-free-books-to-master-statistics-for-data-science-kxdn0n1mj "5 Free Books to Master Statistics for Data Science") Article KDnuggets · 3y 5 Free Books to Master Statistics for Data Science A list of 5 free books to master statistics for data science, covering topics such as sampling, probability, regression, and Bayesian methods. 33 19. 19 [](/posts/the-math-of-card-shuffling-nueg4rdjq "The Math of Card Shuffling") Article Hacker News · 2y The Math of Card Shuffling A deck of cards needs to be riffle shuffled seven times to achieve a sufficiently random order. This is derived from the mathematical concept of permutations, considering a standard deck of 52 cards. Additionally, shuffling one card at a time would require an average of 236 single card riffles to completely randomize the deck. The post references a Numberphile video discussing these and other card shuffling facts. 32 20. 20 [](/posts/math-for-creative-developers-three-js-workshops-z79tooyh6 "Math for creative developers :: Three.js Workshops") Article Community Picks · 2y Math for creative developers :: Three.js Workshops The post offers insights on the application of specific math concepts in visual graphics, particularly in the context of Three.js workshops. 31 1 21. 21 [](/posts/why-democracy-is-mathematically-impossible-ivygez78d "Why Democracy Is Mathematically Impossible") Video Community Picks · 2y Why Democracy Is Mathematically Impossible Democracy may be mathematically impossible due to fundamental flaws in the voting methods we use, a fact established mathematically and leading to a Nobel Prize. The first-past-the-post system, though widely used, often results in unrepresentative outcomes and strategic voting. Alternative methods like ranked-choice voting and instant runoff voting aim to address some issues but still have their own pitfalls. Arrow's impossibility theorem further complicates the matter, proving no ranked-choice method can satisfy all rational criteria simultaneously. However, rated voting systems like approval voting offer a potential solution, increasing voter turnout and reducing negative campaigning while preventing the spoiler effect. 29 4 22. 22 [](/posts/i-tried-the-oldest-programming-language-xpv2yg1vh "I Tried The OLDEST Programming Language") Video Community Picks · 3y I Tried The OLDEST Programming Language The post explores the author's experience learning and experimenting with Fortran, the oldest programming language. It discusses the history, challenges, and results of creating a password generator and a 3D donut. The author also expresses curiosity about the continued use of Fortran despite the availability of newer technologies. 25 3 23. 23 [](/posts/turing-kicked-us-out-of-heaven-axblrtutc "Turing kicked us out of Heaven") Article Lobsters · 2y Turing kicked us out of Heaven The post discusses the significance of the halting problem, an undecidable problem in computer science which states that no algorithm can determine if an arbitrary program with arbitrary input will halt. It explores the implications of this problem on mathematics and programming, illustrating that many long-standing mathematical problems could be solved and programming tasks simplified if the halting problem were solvable. However, due to its undecidability, such advancements remain impossible. Various real-world examples and explanations emphasize the profound impact of this theoretical limitation. 24 24. 24 [](/posts/non-euclidean-doom-what-happens-to-a-game-when-pi-is-not-3-14159--oxhwqmava "Non-Euclidean Doom: what happens to a game when pi is not 3.14159…") Article Hacker News · 2y Non-Euclidean Doom: what happens to a game when pi is not 3.14159… Explore the impact of using an incorrect value of pi in the Doom game and the possibilities of non-Euclidean geometries. 24 2 25. 25 [](/posts/collision-detection-75rwqjww5 "Collision Detection") Article Hacker News · 3y Collision Detection This post explains the algorithms behind collision detection using basic shapes like circles, rectangles, and lines. 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