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Learn CUDA, earn certificates with paid and free online courses from Stanford, Johns Hopkins, Georgia Tech, IIT Kharagpur and other top universities around the world. Read reviews to decide if a class is right for you.
Explore advanced GPU programming techniques for enterprise-scale applications, including multi-GPU systems, asynchronous workflows, and optimized algorithms for sorting and image processing.
Learn to use CUDA's advanced libraries like CuFFT, cuBLAS, and Thrust for complex computations and machine learning applications, including object detection and language translation.
Learn parallel programming with CUDA to process large datasets using GPUs. Explore thread management, memory types, and performance optimization techniques for complex problem-solving on Nvidia hardware.
Learn parallel programming on GPU's with CUDA from basic concepts to advance algorithm implementations.
Learrn to Code with CUDA with GPGPU-Simulators & Docker, Kickstart Your Computing and Data Science Career!
Comprehensive introduction to CUDA programming, covering vector operations, matrix multiplication, memory optimization, and performance tuning for GPU-accelerated computing.
An entry-level course on CUDA - a GPU programming technology from NVIDIA.
Learn to use CUDA C/C++ tools and techniques to accelerate CPU-only applications to run on massively parallel GPUs.
Introducción práctica a la programación GPU con CUDA 10, abarcando fundamentos, modelo de programación, memoria compartida, aprendizaje profundo y concurrencia para aplicaciones de alto rendimiento.
Comprehensive introduction to CUDA programming, covering architecture, memory, terminologies, and practical implementations for vector and matrix operations on GPUs.
Learn to program NVIDIA GPUs with CUDA, covering basics, kernels, memory, parallel algorithms, shared memory, and the Thrust library for efficient GPU computing.
Explore NVIDIA's latest AI tech advancements, including CUDA Graphs, WMMA, and cuDNN. Learn about performance improvements, Tensor Cores, and new features for deep learning acceleration.
Explore GPU computing with CUDA 4.0 and Parallel Nsight 2.0, integrated into Visual Studio for enhanced graphics and parallel application development.
Explore CUTLASS, an open-source CUDA C++ template library for optimizing deep learning computations on NVIDIA GPUs. Learn to develop high-performance custom kernels using Tensor Core programming.
Explore CUDA 12.2's new features: Hopper GPU support, confidential computing, heterogeneous memory management, and MPS prioritization enhancements for efficient, secure, and accelerated application development.
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