Explanation
Specializing in GPU Programming is the graphics processing unit (GPU) in the programming training series published by Coursera Training Academy. This study series consists of four different courses. Graphics cards and their processing units usually have a lot of processing power and can process a lot of different data in a short amount of time. For this reason, these cards are often used for heavy computing or HPC projects. This educational collection can be useful for many people who are active in the fields of data science and software development. Writing software that is flexible and compatible with available hardware is one of the most important skills you should know as a software developer.
During this course, you will learn about CUDA and the libraries that provide features such as parallel processing and iterative processing. These libraries are often used in machine learning applications, signal processing, image and audio, etc.
What you will learn in the GPU Programming Specialization:
- machine learning
- Graphics processing unit (GPU) and its programs
- Comparative Accounting
- Image Processing
- C++ programming language
- Cuda processing and programming platform
- Python programming language
- Strings and their importance in computing
- writing an algorithm
- Nvidia
- data science
- Studying the architecture of the graphics unit (GPU)
- And…
Course Guidelines
Publisher: Course
Teachers: Chancellor Thomas Pascale
Language: English
Level: Medium
Institution/University: Johns Hopkins University
Number of Courses: 4
Duration: Approximately 5 months to complete – recommended pace of 4 hours/week
Courses include:
Course 1
Introduction to Hybrid Programming for GPUs
Course 2
Introduction to Parallel Programming with CUDA
Course 3
CUDA for Business Scale
Course 4
CUDA Advanced Libraries
GPU Programming Specialization Requirements
What background knowledge is necessary?
Prospective students must have at least 1 year of programming experience. A high level of comfort in C/C++ programming will help in absorbing material and completing tasks.
Do I need to take courses in a specific way?
Each specialization course must be completed as follows:
- Introduction to Hybrid Programming for GPUs
- Introduction to Parallel Programming with CUDA
- CUDA for Business Scale
- CUDA Advanced Libraries
Pictures
Video Introduction to GPU Programming Specialists
Installation Guide
After the launch, follow your favorite player
English language
Quality: 720p
This specialization consists of 4 courses.
Download Links
Course 1 – Introduction to Integrated Programming with GPUs
Course 2 – Introduction to CUDA Parallel Programming
Course 3 – CUDA at Business Scale
Course 4 – CUDA Advanced Library
Password file: free download software
size
In total, about 1.5 GB