Educational resources for self-learners
Convex Optimization I, Lecture 1: Introduction and Convex Optimization Overview
Dive into the conceptual foundations of optimization with Professor Stephen Boyd from Stanford University. This lecture covers linear programming (including the Simplex Method as a case study), introduces convex sets and functions, explores the core concepts of convex optimization, and provides an introduction to gradient descent—all crucial for understanding machine learning, product design, and business applications.
Math and Science
A YouTube channel offering in-depth tutorials and explanations across various mathematics and science topics, making complex concepts accessible.
3.1: Introduction to Systems of ODEs
An academic article introducing systems of ordinary differential equations (ODEs), covering their structure, applications, and conversion of higher-order ODEs to first-order systems. From Mathematics LibreTexts.
How Netflix Recommends Movies: Matrix Factorization
This video from Serrano.Academy explains how Netflix uses Matrix Factorization and Gradient Descent for its recommendation system. It's a great resource for understanding mathematical optimization and its application in programming, startups, psychology, and product design, with connections to graph theory.
Lec 24 | MIT 18.03 Differential Equations, Spring 2006
Introduction to First-order Systems of ODE's; Solution by Elimination, Geometric Interpretation of a System. This lecture covers the structure of systems of first-order ODEs, demonstrates how to write them in vector/matrix form, and discusses solution strategies including elimination and a geometric interpretation of first-order systems.
Numerical Methods: Series and Sequences
This PDF document from MIT OpenCourseWare's "Introduction to Numerical Analysis" course covers the fundamental concepts of series and sequences, which are essential for understanding numerical methods. It delves into the theoretical underpinnings necessary for applying these methods in programming and scientific computation.
Lex Fridman Podcast #472: Terence Tao – Hardest Problems in Mathematics, Physics & the Future of AI
Dive into a profound conversation with mathematician Terence Tao on the Lex Fridman Podcast. This episode explores mathematical modeling through the lens of Navier-Stokes equations, the geometry of space in general relativity, and numerical methods in the context of AI-assisted theorem proving. It also delves into the nature of mathematical discovery, problem-solving strategies, and the intersection of mathematics with programming, AI, physics, and philosophy. Perfect for those interested in the foundational and applied aspects of advanced mathematics, and its relevance to technology, startups, psychology, and product design.
Lex Fridman Podcast #472: Terence Tao – Hardest Problems in Mathematics, Physics & the Future of AI
Dive into a profound conversation with mathematician Terence Tao on the Lex Fridman Podcast. This episode explores mathematical modeling through the lens of Navier-Stokes equations, the geometry of space in general relativity, and numerical methods in the context of AI-assisted theorem proving. It also delves into the nature of mathematical discovery, problem-solving strategies, and the intersection of mathematics with programming, AI, physics, and philosophy. Perfect for those interested in the foundational and applied aspects of advanced mathematics, and its relevance to technology, startups, psychology, and product design.
Dr. Trefor Bazett
Dr. Trefor Bazett creates educational videos on various mathematical topics, including calculus and differential equations, with clear explanations.
Math Fortress
Math Fortress provides educational videos on various mathematics topics, including linear algebra, calculus, and more, with a focus on clear explanations and foundational concepts.
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