Description
GPU Programming Specialization is a graphics processing unit (GPU) programming training series published by Coursera Training Academy. This educational series consists of four separate courses. Graphics cards and their processing units usually have a lot of processing power and can process a huge amount of different data in a short period of time. For this reason, these cards are usually used in heavy computing or HPC projects. This educational collection can be useful for many people active in the fields of data science and software development. Writing flexible software that fits the available hardware is one of the most important skills that you should know as a professional software developer.
During this training course, you will get to know CUDA and libraries that provide us with features such as parallel processing and repeated processing. These libraries are usually used in machine learning applications, signal processing, image and sound, etc.
What you will learn in the GPU Programming Specialization course:
- machine learning
- Graphics processing unit (GPU) and its programming
- Parallel Computing
- Image Processing
- C++ programming language (C++)
- Processing and programming platform Cuda
- Python programming language
- Strings and its importance in calculations
- Algorithm writing
- Nvidia
- Data science
- Getting to know the general structure of graphics processing units (GPU)
- And …
Course details
Publisher: Coursera
teacher: Chancellor Thomas Pascale
English language
Providing institution/university: Johns Hopkins University
Education level: Intermediate
Number of courses: 4
Duration of training: assuming 4 hours of work per week, about 5 months
Courses available in the GPU Programming Specialization collection
Course 1
Introduction to Concurrent Programming with GPUs
Course 2
Introduction to Parallel Programming with CUDA
Course 3
CUDA at Scale for the Enterprise
Course 4
CUDA Advanced Libraries
Course prerequisites
What background knowledge is necessary?
Prospective students should have a minimum of 1 year of programming experience. A high level of comfort in programming in C/C++ will help in the absorption of material and completion of assignments.
Do I need to take the courses in a specific order?
Each course in the specialization should be completed in the following order:
- Introduction to Concurrent Programming with GPUs
- Introduction to Parallel Programming with CUDA
- CUDA at Scale for the Enterprise
- CUDA Advanced Libraries
Pictures
GPU Programming Specialization sample video
Installation guide
After extracting, watch with your favorite player.
English subtitle
Quality: 720p
This educational series consists of 4 separate courses.
download link
Course 1 – Introduction to Concurrent Programming with GPUs
Course 2 – Introduction to Parallel Programming with CUDA
Course 3 – CUDA at Scale for the Enterprise
Course 4 – CUDA Advanced Libraries
Password file(s): www.downloadly.ir
The size of the files
About 1.5 GB in total
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