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. Right now, I am in Gold Tier. 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:

• 2/12/21: Fundamentals of Deep Learning (NVIDIA DLI workshop). Instructor: Andrés Díaz-Pinto. Organized at University of Seville.
• 29 & 30/11/21: GPU computing at the Second COMCHA school, at IFIC - University of Valencia (Valencia), including the workshop Fundamentals of Accelerated Computing with CUDA C/C++.
• 23 & 28/04/21: Fundamentals of Accelerated Data Science with RAPIDS (NVIDIA DLI workshop). Inside the Master of Data Science and Big Data, University of Seville (Sevilla, Spain).
• 25/02/21: Fundamentals of Accelerated Data Science with RAPIDS (NVIDIA DLI workshop). Inside SATELEC 2021, at Polytechnic University of Madrid (Spain).
• 8-10/06/20: Fundamentals of Accelerated Computing with CUDA C/C++ (NVIDIA DLI workshop). University of Seville (Sevilla, Spain). Within the GPU Days Universiad de Sevilla. More information, switch the Spanish version of this page.
• 12-13/05/20: Fundamentals of Accelerated Data Science with RAPIDS (NVIDIA DLI workshop). Inside the Master of Data Science and Big Data, University of Seville (Sevilla, Spain).
• 07-09/10/19: GPU computing at the First COMCHA school, including the workshop Fundamentals of Accelerated Computing with CUDA C/C++. University Ramon Llull (Barcelona, Spain)
• 22/05/19: Fundamentals of Accelerated Computing with CUDA C/C++ (NVIDIA DLI workshop). University of Seville (Sevilla, Spain)
• 11/11/14: NVIDIA CUDA Research Center: Applications and Opportunities (talk). University of Seville (Sevilla, Spain)

NVIDIA Deep Learning Institute (DLI),  through the collaboration between the University of Seville and King's College of London, offers you the workshop on Fundamentals of Deep Learning. In this 8-hour workshop you will learn how to define convolutional neural networks, train them with datasets, address overfitting, make image classifiers, transfer learning with pre-trained models, and understand recurrent networks and word embedding. You will work first-hand with Keras and Tensorflow. This workshop includes a final assessment which, if you manage to solve it, you will get an official certificate. Information:

• Date: December 2, 2021, from 8:30 to 16:30
• Place: online (the platform will be announced soom)
• Registration: FULL. You can fill out the form at the end of the website to receive notifications for next editions.
• Cost: free (for academic community)
• Language: Spanish (materials in English)
• Instructors: certified ambassadors of NVIDIA DLI.
  • Instructor principal: Andrés Díaz-Pinto is currently a Research Fellow at King’s College London and part of the London AI centre. In collaboration with NVIDIA, Andres is developing the new MONAILabel platform that facilitates medical image annotation using Deep Learning and Active Learning techniques.
  • Moderador y TA (ayudante): Miguel Ángel Martínez-del-Amor is currently an assitant professor at the department of computer science and artificial intelligence of the University of Seville. His research interest is focused mainly in the interplay of GPU computing, Machine Learning and Bio-inspired Computing.
• Structure of the workshop: visit the official website.

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.

Moreover, if you belong to academia and want to take a free workshop from NVIDIA DLI, check organized workshops for faculty development or for students.

---