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Applied Accelerated Artificial Intelligence

By Prof. Satyajit Das, Prof. Satyadhyan Chickerur, Prof. Bharatkumar Sharma, Prof. Adesuyi Tosin, Prof.Ashrut Ambastha   |   IIT Palakkad, KLE Technological University, NVIDIA, NVIDIA
Learners enrolled: 15201

Technical Partners

  


This course will cover the fundamentals of the compute capabilities and the system software required for implementing artificial intelligence (AI) based solutions on industrial use cases such as the ones in the domains of healthcare and Smart City. The course will discuss end to end deployments of two industrial use cases with demonstration, and hence will help participants use state-of-the-art AI SDKs effectively to solve complex problems.

INTENDED AUDIENCE: Ph.D Scholars (any stream of Science or Engineering); Post Graduate Students (any stream of Science or Engineering); 3rd and 4th year UG Students (any stream of Engineering); Non-Students and Working Professionals

PREREQUISITES: Prior knowledge of Computer Organization, High-Performance; Computing, Machine Learning and Deep learning is desirable

INDUSTRY SUPPORT: Companies working in the domains of Machine Learning and Artificial Intelligence
Summary
Course Status : Ongoing
Course Type : Elective
Duration : 12 weeks
Start Date : 25 Jul 2022
End Date : 14 Oct 2022
Exam Date : 29 Oct 2022 IST
Enrollment Ends : 08 Aug 2022
Category :
  • Computer Science and Engineering
Credit Points : 3
Level : Postgraduate

Page Visits



Course layout

Week 1: Introduction to AI System Hardware CPU, RAM, GPU, Interconnects, Storage, Network Controller; Introduction to AI Accelerators GPUs (Lecture ); Introduction to System Software Operating System, Virtualization, Cloud; ( Lecture )
Week 2: Introduction to Containers and IDE (Jupyter Demo) ( Lecture + Demo ); Scheduling and Resource Management Introduction to schedulers and orchestration tools ( Lecture ); DeepOps: Deep-dive into Kubernetes with deployment of various AI-based services (Lecture + Demo)
Week 3: DeepOps (contd) ( Lecture + Demo ); Design principles for building High Performance compute clusters for AI ( Lecture ); Implementation details for building High Performance compute clusters for AI (contd) (Lecture)
Week 4: Frameworks for Accelerated Deep Learning Workloads - PyTorch ( Lecture ); Frameworks for Accelerated Deep Learning Workloads - PyTorch (contd) ( Lecture + Demo ); Accelerated PyTorch ( Lecture + Demo )
Week 5: Frameworks for Accelerated Deep Learning Workloads - TensorFlow ( Lecture ); Frameworks for Accelerated Deep Learning Workloads - TensorFlow (contd) ( Lecture + Demo ); Accelerated TensorFlow ( Lecture + Demo )
Week 6: Optimizing Deep Learning Training: Automated Mixed Precision ( Lecture + Demo ); Optimizing Deep Learning Training: Transfer Learning ( Lecture + Demo )
Week 7: Fundamentals of Distributed AI Computing: Multi-GPU and multi-node implementation (MPI, NCCL, RDMA) ( Lecture ); Distributed AI Computing: Horovod ( Lecture + Demo )
Week 8: Challenges with Distributed Deep Learning Training Convergence ( Lecture + Demo ); Fundamentals of Accelerating Deployment ( Lecture + Demo)
Week 9: Accelerating neural network inference in PyTorch and TensorFlow ( Lecture + Demo ); Accelerated Data Analytics (Lecture + Demo); Accelerated Machine Learning (Lecture + Demo)
Week 10: Scale Out with DASK; Web visualizations to GPU accelerated crossfiltering ( Lecture + Demo ); Accelerated ETL Pipeline with SPARK
Week 11: Applied AI: Smart City ( Intelligent Video Analytics); Applied AI: Smart City (Intelligent Video Analytics) (Contd.)
Week 12: Applied AI: Healthcare (Federated Learning, AI Assisted Annotation); Applied AI: Healthcare (Federated Learning, AI Assisted Annotation)

Books and references

Nil

Instructor bio

Prof. Satyajit Das

IIT Palakkad
Prof. Satyajit Das is an Assistant Professor in the Department of Computer Science and Engineering, IIT Palakkad. He received his joint Ph.D. degree from University of South Brittany (UBS), France, and University of Bologna (UniBo), Italy. Prior to joining IIT Palakkad, he was a postdoctoral fellow at LabSTICC, UBS.His research spans the areas of architecture, methods, and tools for embedded systems, including CGRAs, custom processors, multi-cores, high-level synthesis, and compilers. The main focus of Dr. Das's research is to implement highly energy efficient solutions for digital architectures in the domain of heterogeneous and reconfigurable multi-core System on Chips (SoCs). This includes architectures, design implementation strategies, runtime, and compilation support.


Prof. Satyadhyan Chickerur

KLE Technological University
Prof. Satyadhyan Chickerur received his B.E degree in E&C, M.Tech in CSE and PhD in Computer and Information Sciences. He is presently Professor at School of Computer Science and Engineering and head of Centre for High Performance Computing at KLE Technological University, Hubli. He has served as faculty in various engineering colleges in India. He is a member of ISTE, IEEE and ACM. He was the Execom member of IEEE signal processing society, Bangalore chapter (2007-2009). He was a Member of Intel - IISC - VTU multicore Curriculum Development committee. He was one of the judges and problem setter for ACM ICPC programming contest of the Asia Regional's in the year 2007 and 2008. He has received various grants from industry and other Organisations for research as well.


Prof. Bharatkumar Sharma

NVIDIA
Prof. Bharatkumar Sharma obtained a master's degree in information technology from the Indian Institute of Information Technology, Bangalore. He has around 10 years of development and research experience in the domains of software architecture and distributed and parallel computing. He is currently working with NVIDIA as a senior solutions architect, South Asia.


Prof. Adesuyi Tosin

Prof. Tosin Adesuyi currently works at the Department of Computer and Software Engineering, Kumoh National Institute of Technology. Prof. Tosin does research in Deep Learning, Data Science, Data Mining and Computer Security. Their current project is Privacy in deep neural networks '


Prof.Ashrut Ambastha

NVIDIA
Prof.Ashrut Ambastha is a Principal Engineer at NVIDIA working on networking fabric for HPC clusters and AI datacenters. He is also a member of the application engineering team that works on product designs with networking silicon devices. He holds an MSc and MTech in Electrical Engineering from Indian Institute of Technology-Bombay.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams:  29 October 2022 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras. It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team


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