Coursera – GPU Programming Specialization

使用强大的 GPU 解决挑战。掌握高性能计算并应用于众多领域

您将学到什么
开发 CUDA 软件以在常用硬件上运行大量计算

利用将众所周知的算法引入软件的库，而无需重新开发现有功能

学生将学习如何使用 Python 和 C/C++ 编程语言开发并发软件。

学生将获得对 GPU 硬件和软件架构的入门级理解。

您将获得的技能
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

关于本专业化
5,207 次近期浏览
此专业化旨在帮助数据科学家和软件开发人员创建使用常用硬件的软件。将向学生介绍 CUDA 和允许并行和快速执行大量计算的库。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 2 个项目，使他们能够自由探索基于 CUDA 的图像/信号处理解决方案，以及一个选择主题，这些主题可以来自他们当前或未来的职业生涯。他们还将创建简短的演示来展示他们的努力并分享他们的代码。

Solve Challenges with Powerful GPUs. Develop mastery in high performance computing and apply to numerous fields

Last updated 12/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 122 Lessons (9h 45m) | Size: 1.7 GB

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
• S​tudents will learn how to develop concurrent software in Python and C/C++ programming languages.
• S​tudents 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

L​earners 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.