Course Objective:
This Faculty Development Programme (FDP) is designed to equip participants with the knowledge and hands-on skills required to leverage High-Performance Computing (HPC) for cutting-edge Machine Learning (ML) and Deep Learning (DL) projects. With the exponential growth of data and the increasing complexity of AI models, HPC systems play a critical role in enabling faster training, large-scale experimentation, and efficient deployment.
The programme introduces participants to HPC architectures, parallelization strategies, distributed training frameworks, and resource management techniques. Participants will gain practical exposure to setting up ML/DL environments on HPC clusters, training deep neural networks across multiple GPUs/CPUs, and handling large-scale datasets. Real-world case studies from domains such as natural language processing, computer vision, climate modeling, and genomics will be explored to highlight the impact of HPC in AI-driven research.
By the end of the programme, participants will be able to:
* Understand HPC fundamentals and their applications in ML/DL
* Utilize parallel and distributed training methods effectively
* Optimize computation and resource usage in HPC environments
* Implement large-scale ML/DL pipelines on HPC systems
* Apply HPC-powered AI techniques to real-world datasets and research problems
Syllabus:
- Introduction to High-Performance Computing (HPC) for AI
- Parallelization Techniques in Machine Learning
- Deep Learning on HPC Systems
- Big Data and HPC for ML/DL Projects
- Case Studies and Hands-On Projects
Resource Persons
- Dr. Nagaiah Chamakuri, IISER, Trivandrum
- Dr. John Paul, IIIT, Kottayam
- Dr. Vishnu Anilkumar, IIIT, Kottayam
How to apply
The application included herewith or in a similar format, duly recommended by the head of the Institution should be uploaded through the Registration link (Click Here). The selected candidates will be intimated through email. The number of seats is limited to 30.
There will be no registration fee. TA will be provided to participants as per Government rules.
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