Getting Started with cuDNN and NVCC in C++

Accelerated computing powers modern deep learning and high-performance applications, but setting up your development environment correctly is often the hardest hurdle. This text-based course guides you through configuring cuDNN, working with the NVCC compiler, and writing your first GPU-accelerated C++ programs. You will build a clear understanding of hardware acceleration workflows and gain confidence compiling custom C++ code for parallel computing. What you will learn: Understand the core architecture of GPU acceleration and deep learning primitives; Configure and verify cuDNN installations alongside the development toolkit; Compile and link C++ programs using the NVCC command-line compiler; Manage memory transfers between host and device in modern C++; Practice building and running foundational GPU computing routines; Troubleshoot common linker, driver, and environment configuration errors. The course starts with essential terminology and fundamental hardware concepts before moving systematically into configuration steps and hands-on coding exercises. This course is designed for programmers and developers who are new to GPU computing and want a structured, step-by-step introduction without complex prerequisites. Start reading today to build your foundation in accelerated computing and deep learning development.