Deep Learning Institute is an organization
dedicated to provide training on GPU-related topics. They
offer hands-on courses with cloud resources, which are a great
way to get into a new technology. In addition, the platform
generates a certificate upon completion of a proposed final
I am an University Ambassador since 2018, and I collaborate with them providing training on GPU computing, Deep Learning and Data Science. If you are interested in receiving training on any of the following courses, for which I am a certified instructor, please contact me.
Fundamentals of Accelerated Computing with CUDA C/C++
The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by:
Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast. An official certification from NVIDIA DLI will be issued after completing the assessment. Official website: here
Fundamentals of Deep Learning
In this instructor-led course, you will learn the basics of deep learning by training and deploying neural networks. You will:
An official certification from NVIDIA DLI will be issued after completing the assessment. This is a only instructor-led workshop, not available for self pace. Official website: here. This course is the replacement of "Fundamentals of Deep Learning for Computer Vision".
Fundamentals of Accelerated Data Science with RAPIDS
In this hands-on course, you will learn to GPU-accelerate end-to-end data science workflows by:
An official certification from NVIDIA DLI will be issued after completing the assessment. Official website: here.
The NVIDIA Academic Hardware Grant program has selected my project entitled "Deep Learning for master students at University of Seville", for which they will donate an A100 PCIe GPU. This graphics card has 6912 cores and 40GB of memory, and it is optimized to speedup the training of deep neural networks. The students that will benefit from this hardware will be prioritized as follows:
I have given and organized the following GPU-related courses and talks: