cuFFT provides the ability to perform fast Fourier transforms (FFTs) on large datasets. Students will learn of common use cases such as fast multiplication of large polynomials, signal processing, and matrix operations. They will use this library to develop software that process audio or video signals.

The CUDA Toolkit includes a number of linear algebra libraries, such as cuBLAS, NVBLAS, cuSPARSE, and cuSOLVER. Students will learn the different capabilities and limitations of many of them and apply that knowledge to compute matrix dot products, determinant, and finding solutions to complex linear systems.

Most developers utilize data structures beyond the primitives and pointers that make up the core of CUDA programmers, which makes pure CUDA development difficult. Students will learn about the Thrust library that adds the vector data structure and associated algorithms that allow for simplification of their code. Students will create software that transform, reduction, and sort large datasets.

Data Scientists, Machine Learning, and Artificial intelligence experts are using neural networks to solve problems such as human language translation, image classification, and object detection/avoidance. Using the cuDNN and cuTensor, students will be able to develop a variety of neural networks and similar structures. At the completion of this module students will be asked to develop a course-wide project that brings together their knowledge from all previous courses and lessons to develop a capstone software project.