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Applied Accelerated Artificial Intelligence

By Prof. Satyajit Das, Prof. Satyadhyan Chickerur, Prof. Bharatkumar Sharma, Prof. Adesuyi Tosin   |   IIT Palakkad, KLE Technological University, NVIDIA
Learners enrolled: 6424   |  Exam registration: 1131

Technical Partners

  


ABOUT THE COURSE:
This course will cover the fundamentals of the compute capabilities and the system software required for implementing artificial intelligence (AI) based solutions with accelerated computing on industrial use cases such as the one in the domains of Smart City. The course will discuss end to end deployments of industrial use cases like using large language models 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 : Completed
Course Type : Elective
Language for course content : English
Duration : 12 weeks
Category :
  • Computer Science and Engineering
Credit Points : 3
Level : Postgraduate
Start Date : 22 Jul 2024
End Date : 11 Oct 2024
Enrollment Ends : 05 Aug 2024
Exam Registration Ends : 16 Aug 2024
Exam Date : 27 Oct 2024 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.


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Course layout

Week 1: Introduction to AI Systems Hardware part 1,Introduction to AI Systems Hardware part 2,Introduction to AI Accelerators,GPUs,Introduction to Operating Systems, Virtualization, Cloud part 1,Introduction to Operating Systems, Virtualization, Cloud part 2

Week 2: Introduction to Containers and IDE Dockers part1,Introduction to Containers and IDE Dockers part 2,Scheduling and Resource Management part 1,Scheduling and Resource Management part 2,DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services Part 1,DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services Part 2

Week 3: DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services Session II part 1, DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services Session II part 2, Design principles for Building High Performance Clusters part 1, Design principles for Building High Performance Clusters part 2, Design principles for Building High Performance Clusters part 3, Design principles for Building High Performance Clusters part 4

Week 4: Introduction to Pytorch part 1,Introduction to Pytorch part 2,Introduction to Pytorch part 3,Introduction to Pytorch part 4,Profiling with DLProf Pytorch Catalyst part 1,Profiling with DLProf Pytorch Catalyst part 2

Week 5: Introduction to TensorFlow part 1,Introduction to TensorFlow part 2,Accelerated TensorFlow,Accelerated TensorFlow,Accelerated TensorFlow -  XLA Approach,Accelerated TensorFlow -  XLA Approach

Week 6: Optimizing Deep learning Training: Automatic Mixed Precision part 1,Optimizing Deep learning Training: Automatic Mixed Precision part 2,Optimizing Deep learning Training: Transfer Learning part 1,Optimizing Deep learning Training: Transfer Learning part 2

Week 7: Fundamentals of Distributed AI Computing Session 1 Part 1, Fundamentals of Distributed AI Computing Session 1 Part 2, Fundamentals of Distributed AI Computing Session 2 Part 1, Fundamentals of Distributed AI Computing Session 2 Part 2, Distributed Deep Learning using Tensorflow and Horovod

Week 8: Challenges with Distributed Deep Learning Training Convergence, Fundamentals of Accelerating Deployment part 1, Fundamentals of Accelerating Deployment part 2

Week 9: Accelerating neural network inference in PyTorch and TensorFlow part 1, Accelerating neural network inference in PyTorch and TensorFlow part 2, Accelerated Data Analytics part 1, Accelerated Data Analytics part 2, Accelerated Data Analytics part 3, Accelerated Data Analytics part 4, Accelerated Machine Learning

Week 10: Introduction  to NLP part 1 ,Introdcution to NLP part 2 

Week 11: Applied AI: Smart City ( Intelligent Video Analytics) Session 1 part 1,Applied AI: Smart City ( Intelligent Video Analytics)  Session 1 part 2,Applied AI: Smart City ( Intelligent Video Analytics) Session 2 Deepstream  part 1,Applied AI: Smart City ( Intelligent Video Analytics) Session 2 Deepstream  part 2

Week 12: Introduction to word embedding,Text classification using word embedding

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 '

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: 
27 October 2024 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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