The NVIDIA 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 exercise.

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:

  • Accelerating CPU-only applications to run their latent parallelism on GPUs
  • Utilizing essential CUDA memory management techniques to optimize accelerated applications
  • Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
  • Leveraging command line and visual profiling to guide and check your work

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:

  • Learn the fundamental techniques and tools required to train a deep learning model.
  • Gain experience with common deep learning data types and model architectures.
  • Enhance datasets through data augmentation to improve model accuracy.
  • Leverage transfer learning between models to achieve efficient results with less data and computation.
  • Build confidence to take on your own project with a modern deep learning framework.

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:

  • Using cuDF, Dask, and BlazingSQL to injest and manipulate massive datasets directly on the GPU.
  • Utilizing a wide variety of GPU-accelerated machine learning algorithms including XGBoost, cuGRAPH, and several cuML algorithms to perform data analysis at massive scale.
  • Performing multiple analysis tasks on several massive datasets in an effort to stave off a simulated epidemic outbreak effecting the entire UK population.

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:

  • Students attending the course Deep Learning of the Computer Engineering Master, in order to develop their assessments with the enough resources.
  • Students attending one of the courses I will organize in the future, where I will use the workshops of NVIDIA DLI and specialized material beyond these workshops.
  • Students developing their master thesis at the Computer Engineering Master and the Logic, Computing and Artificial Intelligence Master, that need to train Deep Learning models, accelerate Data Science workflows with RAPIDS or speedup code with CUDA.
  • Bachelor students at ETSII that need this hardware to train Deep Learning models, accelerate Data Science workflows with RAPIDS, or speedup code with CUDA.


I have given and organized the following GPU-related courses and talks:



You can use the following form (in Spanish) to show your interest on the workshops described above. In this way, you will be in the wait list for the next editions (planned for next fall 2021). For personal issues, I couldn't organized the GPU days events yet, sorry for the inconveniences.