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CUDA Advanced Libraries
This course is part of GPU Programming Specialization.
Course Cost
Free course
Intermediate
Skill Level
22 Hours
Self-paced lessons
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full GPU Programming Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
English
What you'll learn
Use cuFFT for signal and image processing
Implement linear algebra operations with cuBLAS
Develop data structures using Thrust library
Create neural networks with cuDNN and cuTensor
Build complete machine learning applications
Skills you'll gain
This course includes:
2.2 Hours PreRecorded video
1 peer review, 4 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
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Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
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There are 5 modules in this course
This advanced course focuses on mastering CUDA's high-performance libraries for complex computational tasks. Students learn to implement Fast Fourier Transforms with cuFFT, perform advanced linear algebra operations using cuBLAS and related libraries, utilize the Thrust library for efficient data structures, and develop machine learning applications with cuDNN and cuTensor. The curriculum culminates in a capstone project integrating multiple CUDA libraries.
Course Overview
Module 1 · 4 Hours to complete
cuFFT
Module 2 · 3 Hours to complete
CUDA Linear Algebra
Module 3 · 3 Hours to complete
The CUDA Thrust Library
Module 4 · 3 Hours to complete
CUDA Machine Learning
Module 5 · 9 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: GPU Programming Specialization
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Frequently asked Questions
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