Hacker News·2yYou 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.
Towards AI·2yThe 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.
freeCodeCamp·2yDeep 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.
freeCodeCamp·2yPractical 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.
Medium·2yLinear 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.
Daily Dose of Data Science | Avi Chawla | Substack·2y25 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.
Hacker News·2ySrinivasa 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.
Hacker News·2yThe 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.
Community Picks·2yWhy 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.
Community Picks·2yI 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.
Lobsters·2yTuring 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.
