Welcome to Ray Tracing
Introduction to ray tracing techniques for generating photo-realistic digital imagery, covering setup, rendering, performance optimization, and GPU acceleration using C++ and Vulkan.
Vector-Dynamic Array - Making Data Structures in C++
Learn to implement a dynamic array data structure in C++, covering reallocation, push/pop operations, and memory management techniques.
Vector and Matrix Operations
Explore essential linear algebra concepts for neural networks: vectors, matrices, and operations like scalar manipulation, dot product, and matrix multiplication.
Statistical Rethinking 2023 - Modeling Events
Explore statistical modeling for events, covering discrimination, mediation, generative models, and generalized linear models. Learn to analyze and compute interventions, with a bonus on survival analysis.
Statistical Rethinking 2023 - Geocentric Models
Explore geocentric models in statistical thinking, covering Gaussian distributions, workflows, generative models, and analysis techniques for improved statistical reasoning.
Statistical Rethinking 2023 - The Garden of Forking Data
Explore Bayesian statistics through the "Garden of Forking Data" metaphor. Learn about generative models, probability, testing, and posterior distributions for improved statistical reasoning and prediction.
Introduction to 2D Arrays in C++ - LeetCode Questions
Comprehensive tutorial on 2D arrays in C++, covering creation, input/output, searching, and solving LeetCode problems. Includes practical coding examples and problem-solving strategies for matrix operations.
Statistical Rethinking 2022 - Multi-Multilevel Models
Explore advanced statistical concepts including multiple cluster types, multilevel predictions, and non-centered priors in this comprehensive lecture on multi-multilevel models.
Statistical Rethinking 2022 - Modeling Events
Explore statistical modeling of events, covering generative models, generalized linear models, logistic regression, and binomial regression. Learn to analyze gender applications and calculate marginal causal effects.
Statistical Rethinking 2022 - Geocentric Models
Explore Bayesian linear regression, covering model language, generative and statistical approaches, validation, and posterior predictions for a comprehensive understanding of statistical modeling.
Bayesian Inference Is Just Counting
Explore foundations of Bayesian data analysis, focusing on conceptual understanding and causal inference. Learn key principles from renowned expert Richard McElreath's book, covering topics like T-tests and machine learning.
Finding Low-Rank Matrices - From Matrix Completion to Recent Trends
Explore low-rank matrix recovery, from matrix completion to recent trends, covering theoretical foundations and applications in signal processing and machine learning.
Interior Point Methods
Explore advanced interior point methods for optimization, covering key ideas, path following analysis, and specialized algorithms like Renegar's and regularized John ellipse barrier.
Scaling Up Bayesian Inference for Big and Complex Data
Explore advanced Bayesian inference techniques for large-scale datasets, focusing on computational challenges and innovative solutions in machine learning and deep learning applications.
Perceptual Organization From a Bayesian Point of View
Explore Bayesian approach to perceptual organization, examining how the visual system groups images into distinct units through inverse inference and mixture estimation techniques.
Designing with Complex Geometry - Class 3
Explore advanced techniques in Grasshopper for creating complex geometries, including grids, cells, and surface morphing. Hands-on exercises reinforce learning and practical application of concepts.
Addition, Multiplication and Division in Galois Fields GF(2^m)
Learn essential operations in Galois Fields GF(2^m), including addition, multiplication, and division. Explore polynomials, primitives, and their applications in encryption.
Convolution and the Fourier Series
Explore convolution and its relation to Fourier series, unraveling complex mathematical concepts through clear explanations and real-world analogies.
Convex Optimization IV - Sebastien Bubeck, Microsoft Research
Explore advanced concepts in convex optimization, including gradient flow, comparison techniques, and optimal algorithms with Sebastien Bubeck from Microsoft Research.
Digital Circuits Boolean Algebra
Learn Boolean algebra fundamentals and digital circuit simplification techniques, including key rules and operations for efficient logic design and analysis.
Kernel Methods for Causal Inference
Explore kernel ridge regression for causal inference, covering dose responses, treatment effects, and extensions to various settings. Learn about consistency, efficiency, and robustness in estimation.