CUDA Advanced Libraries from Coursera

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Syllabus

Course Overview

The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.

cuFFT

cuFFT provides the ability to perform fast Fourier transforms (FFTs) on large datasets. Students will learn of common use cases such as fast multiplication of large polynomials, signal processing, and matrix operations. They will use this library to develop software that process audio or video signals.

CUDA Linear Algebra

The CUDA Toolkit includes a number of linear algebra libraries, such as cuBLAS, NVBLAS, cuSPARSE, and cuSOLVER. Students will learn the different capabilities and limitations of many of them and apply that knowledge to compute matrix dot products, determinant, and finding solutions to complex linear systems.

The CUDA Thrust Library

Most developers utilize data structures beyond the primitives and pointers that make up the core of CUDA programmers, which makes pure CUDA development difficult. Students will learn about the Thrust library that adds the vector data structure and associated algorithms that allow for simplification of their code. Students will create software that transform, reduction, and sort large datasets.

CUDA Machine Learning

Data Scientists, Machine Learning, and Artificial intelligence experts are using neural networks to solve problems such as human language translation, image classification, and object detection/avoidance. Using the cuDNN and cuTensor, students will be able to develop a variety of neural networks and similar structures. At the completion of this module students will be asked to develop a course-wide project that brings together their knowledge from all previous courses and lessons to develop a capstone software project.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops core machine learning, data science, and artificial intelligence skills, which are highly relevant in academic and industry settings

Uses real-world examples and case studies to help learners understand how these technologies are being used in the field

Includes interactive exercises and hands-on labs to help learners apply their knowledge and skills

Provides opportunities for learners to network with industry professionals and experts

Covers the latest trends and advancements in machine learning, data science, and artificial intelligence

Requires learners to have a strong foundation in computer science and programming

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in CUDA Advanced Libraries with these activities:

CUDA Resources Compilation

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Create a compilation of useful CUDA resources, such as tutorials, documentation, and code samples.

Browse courses on CUDA

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Search for CUDA resources online.
Organize the resources into a document or online repository.
Share your compilation with other learners.

Review Linear Algebra Concepts

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Review linear algebra concepts to strengthen your understanding of CUDA Linear Algebra.

Browse courses on Linear Algebra

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Review your lecture notes or textbooks on linear algebra.
Solve practice problems and exercises.
Identify and focus on areas where you need additional practice.

CUDA by Example: An Introduction to General-Purpose GPU Programming

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Read CUDA by Example to gain a foundational understanding of CUDA programming and its applications.

View Cuda by Example on Amazon

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Purchase or borrow the book.
Read the book thoroughly.
Complete the exercises in the book.
Apply what you've learned to your own projects.

Six other activities

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CUDA Workshop

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Attend a CUDA workshop to learn the basics of CUDA programming and how to use it to accelerate computationally heavy tasks.

Browse courses on CUDA

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Register for a CUDA workshop.
Prepare for the workshop by reviewing the prerequisites.
Attend the workshop and participate actively.
Complete the hands-on exercises during the workshop.
Follow up with the workshop instructors if you have any questions.

Develop a simple image processing application using cuFFT

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Apply the knowledge of cuFFT learned in the course by developing a practical image processing application.

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Design the application and its functionality
Obtain images and convert them into a suitable format for the application
Implement image processing algorithms using cuFFT
Build the user interface and integrate all components
Test and refine the application

Coding Challenges on LeetCode

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Practice coding problems on LeetCode to solidify your understanding of the CUDA Toolkit libraries and machine learning algorithms.

Browse courses on CUDA

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Identify the problem statement and constraints.
Design a solution using CUDA libraries and techniques.
Implement the solution in code.
Test and debug your code.

NVIDIA Developer Tutorials

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Complete the NVIDIA Developer Tutorials to learn how to use the CUDA Toolkit libraries and machine learning algorithms.

Browse courses on CUDA

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Identify the relevant tutorials for your learning goals.
Study the tutorials and take notes.
Complete the hands-on exercises in the tutorials.
Apply what you've learned to your own projects.

CUDA Study Group

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Join a CUDA study group to collaborate with other learners and reinforce your understanding of the material.

Browse courses on CUDA

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Find or start a CUDA study group.
Meet regularly with your study group.
Discuss the course material and work on projects together.

Neural Network Project

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Develop a neural network using cuDNN and cuTensor to solve a real-world problem, such as object detection or natural language translation.

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Define the problem and gather data.
Design the neural network architecture.
Train the neural network.
Evaluate the performance of the neural network.
Deploy the neural network for real-world use.

Career center

Learners who complete CUDA Advanced Libraries will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their understanding of data and related techniques to solve a wide range of problems. They can work in many industries, including healthcare, finance, and manufacturing. The CUDA Advanced Libraries course would be a valuable addition to the training of a Data Scientist because it teaches how to use FFTs, linear algebra libraries, and data structures to perform complex mathematical computations.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. They work on a variety of tasks, including data preparation, model training, and model deployment. The CUDA Advanced Libraries course would be beneficial to Machine Learning Engineers because it teaches how to use cuDNN and cuTensor to develop neural networks and similar structures.

See salaries and explore the career path for Machine Learning Engineer

Developer

Developers use CUDA to develop software applications that run on GPUs. They work in a variety of industries, including gaming, finance, and healthcare. The CUDA Advanced Libraries course would be valuable to Developers because it teaches how to use Thrust library adds the vector data structure and associated algorithms that allow for simplification of their code.

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Software Architect

Software Architects design and develop software applications. They work on a variety of tasks, including requirements gathering, system design, and code development. The CUDA Advanced Libraries course would be useful to Software Architects because it teaches how to use advanced CUDA libraries to develop high-performance software applications.

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Technical Lead

Technical Leads lead teams of developers. They are responsible for the technical direction of a project and for ensuring that the project is completed on time and within budget. The CUDA Advanced Libraries course would be useful to Technical Leads because it teaches how to use advanced CUDA libraries to develop high-performance software applications.

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Database Administrator

Database Administrators design, develop, and maintain database systems. They work on a variety of tasks, including data management, performance tuning, and security. The CUDA Advanced Libraries course would be useful to Database Administrators because it teaches how to use linear algebra libraries to perform complex mathematical computations.

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Network Engineer

Network Engineers design, develop, and maintain computer networks. They work on a variety of tasks, including network design, network implementation, and network troubleshooting. The CUDA Advanced Libraries course would be useful to Network Engineers because it teaches how to use CUDA libraries to develop high-performance software applications.

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Systems Engineer

Systems Engineers design, develop, and maintain computer systems. They work on a variety of tasks, including hardware selection, software installation, and system troubleshooting. The CUDA Advanced Libraries course would be useful to Systems Engineers because it teaches how to use CUDA libraries to develop high-performance software applications.

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Computer Scientist

Computer Scientists research and develop new computer technologies. They work on a variety of tasks, including algorithm design, software development, and hardware design. The CUDA Advanced Libraries course would be useful to Computer Scientists because it teaches how to use advanced CUDA libraries to develop high-performance software applications.

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Electrical Engineer

Electrical Engineers design, develop, and maintain electrical systems. They work on a variety of tasks, including power generation, transmission, and distribution. The CUDA Advanced Libraries course may be useful to Electrical Engineers because it teaches how to use CUDA libraries to develop high-performance software applications.

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Chemist

Chemists study the composition, structure, and properties of matter. They work on a variety of tasks, including synthesizing new compounds, studying the reactions of chemicals, and developing new materials. The CUDA Advanced Libraries course may be useful to Chemists because it teaches how to use CUDA libraries to perform complex mathematical computations.

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Mathematician

Mathematicians develop new mathematical theories and solve mathematical problems. They work on a variety of tasks, including pure mathematics, applied mathematics, and statistics. The CUDA Advanced Libraries course may be useful to Mathematicians because it teaches how to use CUDA libraries to perform complex mathematical computations.

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Astronomer

Astronomers study the universe. They work on a variety of tasks, including observing the stars, planets, and galaxies. The CUDA Advanced Libraries course may be useful to Astronomers because it teaches how to use CUDA libraries to perform complex mathematical computations.

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Biologist

Biologists study living organisms. They work on a variety of tasks, including studying the structure, function, and evolution of organisms. The CUDA Advanced Libraries course may be useful to Biologists because it teaches how to use CUDA libraries to perform complex mathematical computations.

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Physicist

Physicists study the laws of nature. They work on a variety of tasks, including theoretical physics, experimental physics, and applied physics. The CUDA Advanced Libraries course may be useful to Physicists because it teaches how to use CUDA libraries to perform complex mathematical computations.

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CUDA Advanced Libraries

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