CUDA Crash Course
Comprehensive introduction to CUDA programming, covering vector operations, matrix multiplication, memory optimization, and performance tuning for GPU-accelerated computing.
Basic Linear Algebra
Linear Algebra is a basic course for students intended to go for Engineering studies and higher studies in Mathematics. We will cover the basic tools needed in these branches.
Linear Algebra
Comprehensive exploration of linear algebra concepts, from basic operations to advanced topics like eigenvalues, with practical applications and problem-solving techniques.
Fortran with OpenMP
Learn parallel programming in Fortran using OpenMP. Master threads, clauses, work sharing, and optimization techniques for high-performance computing applications.
CUDA
Comprehensive introduction to CUDA programming, covering architecture, memory, terminologies, and practical implementations for vector and matrix operations on GPUs.
Matrix Algebra
Learn essential matrix algebra concepts, from multiplication to subspaces, with practical applications in linear transformations and data analysis.
Matrices and Determinants Class XII 12th CBSE/ISC Maths
Comprehensive exploration of matrices and determinants for 12th grade mathematics, covering key concepts, operations, properties, and problem-solving techniques.
Matrices
Explore matrix operations and applications, from solving systems of equations to matrix multiplication and division, using a TI-84+ calculator.
Matrix Algebra
Master essential matrix algebra concepts, from basic operations to advanced techniques like Gauss elimination, matrix inversion, and Cramer's Rule.
Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2
Comprehensive introduction to TensorFlow and deep learning fundamentals, covering tensor operations, neural network regression, and classification. Hands-on coding examples and practical insights for building and improving models.
Tricks and Tips in Numerical Computing - Keynote
Explore advanced numerical computing techniques, from complex differentiation to low-precision arithmetic, with insights on error analysis, matrix operations, and IEEE standards for improved computational accuracy and efficiency.
3D on Apple II
Explore 3D graphics on Apple II+ using AppleSoft BASIC. Learn matrix math, rotation techniques, and cube rendering in this nostalgic coding challenge.
Machine Learning for Computing Tensor Rank
Explorez l'utilisation de l'apprentissage par renforcement pour découvrir de nouveaux algorithmes de multiplication matricielle et calculer le rang des tenseurs.
FSSpMDM - Accelerating Small Sparse Matrix Multiplications by Run-Time Code Generation
Explore code generation strategies for accelerating small sparse matrix multiplications in high-order finite element solvers, focusing on performant operator kernels for prismatic and hexahedral elements.
Accelerators Made Easy - Improving MLOps at Loblaw Digital Using Vertex AI
Leverage Vertex AI for efficient matrix multiplication in MLOps. Build scalable recommendation engines using Google Cloud Platform's tools for improved product relevance.
Arrays and Matrices II - Data Structures in Mathematics Math Foundations
Explore matrices from traditional linear algebra to a novel data structure approach. Learn matrix multiplication, algebra, and a new perspective using pixels and maxels.
An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games
Explore advanced cutting plane methods for convex optimization and games, covering improved algorithms, runtime bottlenecks, data structures, and practical applications in computational mathematics.
Math4ML Exercises - Linear Algebra, Continued
Hands-on linear algebra exercises for machine learning, covering matrix operations, shape-checking, composition, and practical applications in coding and ML contexts.
Breakthroughs - A Refined Laser Method and Faster Matrix Multiplication
Explore advanced techniques in matrix multiplication, including the refined Laser method, to enhance computational efficiency and speed in mathematical operations.
Matrix Multiplication, Signals, and Convolutions
Explore matrix multiplication, natural signals, and convolutions in deep learning. Includes practical demonstrations using IPython and PyTorch.
Matrix Multiplication - C Programming
Learn to implement matrix multiplication in C, covering algorithm design, memory allocation, and efficient coding techniques for handling multi-dimensional arrays.