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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/gpu-programming
课程评论：没有评论
课程名称：GPU编程 课程概述： 本课程将教您如何开发CUDA软件，以便在常见硬件上运行大规模计算。您将学习利用库进行软件开发，这些库可以快速实现众所周知的算法，而无需重新开发功能。学生将掌握使用Python和C/C++编程语言开发并发软件的技能。此外，课程还将帮助学生对GPU硬件和软件架构有一个初步的理解。 您将获得的技能包括： - 机器学习 - GPU - 并行计算 - 图像处理 - C++ - CUDA - Python编程 - 线程（计算） - 算法 - C/C++ - Nvidia - 数据科学 课程特别说明： 此课程旨在为数据科学家和软件开发人员创建能够利用常见硬件的软件。学生将学习CUDA及其库，以便快速进行大规模并行计算。这些技能的应用包括机器学习、图像/音频信号处理和数据处理。 应用学习项目： 学习者将完成至少两个项目，探索针对图像/信号处理的CUDA解决方案，并选择与自己当前或未来职业相关的主题进行研究。学习者还将创建简短的演示，并分享他们的代码。 证书： 完成课程后，可获得可分享证书。课程完全在线，灵活的学习时间安排使您可以随时开始学习并维护灵活的截止日期。 课程内容： 课程大约需要5个月时间完成，推荐的学习节奏为每周4小时。参与者需具备至少一年的计算机编程经验，最好会C/C++编程语言。 该课程主要以英语授课，并提供英文字幕。
Course Link: https://www.coursera.org/learn/introduction-to-concurrent-programming
Name:Introduction to Concurrent Programming with GPUs
Description:Offered by Johns Hopkins University. This course will help prepare students for developing code that can process large amounts of data in ... Enroll for free.
Course Link: https://www.coursera.org/learn/introduction-to-parallel-programming-with-cuda
Name:Introduction to Parallel Programming with CUDA
Description:Offered by Johns Hopkins University. This course will help prepare students for developing code that can process large amounts of data in ... Enroll for free.
Course Link: https://www.coursera.org/learn/cuda-at-scale-for-the-enterprise
Name:CUDA at Scale for the Enterprise
Description:Offered by Johns Hopkins University. This course will aid in students in learning in concepts that scale the use of GPUs and the CPUs that ... Enroll for free.
Course Link: https://www.coursera.org/learn/cuda-advanced-libraries
Name:CUDA Advanced Libraries
Description:Offered by Johns Hopkins University. This course will complete the GPU specialization, focusing on the leading libraries distributed as part ... Enroll for free.
What you will learn
Develop CUDA software for running massive computations on commonly available hardware
Utilize libraries that bring well-known algorithms to software without need to redevelop existing capabilities
Students will learn how to develop concurrent software in Python and C/C++ programming languages.
Students will gain an introductory level of understanding of GPU hardware and software architectures.
Skills you will gain
Machine Learning
GPU
Parallel Computing
Image Processing
C++
Cuda
Python Programming
Thread (Computing)
Algorithms
C/C++
Nvidia
Data Science
About this Specialization
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This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.
Applied Learning Project
Learners will complete at least 2 projects that allow them the freedom to explore CUDA-based solutions to image/signal processing, as well as a topic of choosing, which can come from their current or future professional career. They will also create short demonstrations of their efforts and share their code.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
At least 1 year of computer programming experience, preferrably with the C/C++ programming language.
Hours to complete
Approximately 5 months to complete
Suggested pace of 4 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
At least 1 year of computer programming experience, preferrably with the C/C++ programming language.
Hours to complete
Approximately 5 months to complete
Suggested pace of 4 hours/week
Available languages
English
Subtitles: English
