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Program Chair
Swagatam Das
Program Coordinator
Partha Pratim Mohanta
Advisory Committee
Sanghamitra Bandyopadhyay
Bhabatosh Chanda
Dipti Prasad Mukherjee
Naqueeb Ahmad Warsi
Pinakpani Pal
Sumana Ghosh
Utpal Garain
Arpan Mukhopadhay
Organizing Chairs
Aditya Panda
Anish Chakrabarty
Faizanuddin Ansari
Kushal Bose
Susmita Ghosh
Priyobrata Mondal
Administrative Chairs
Priyobrata Mondal
Arghya Pratihar
Finance Chairs
Dipesh Chanda
Sekhar Sarkar
Speakers
Professors, Scientists, Post-docs and Research Scholars from ISI, other eminent institutions and R&D labs.
Organizing Committee
Sourav Raha
Arghya Pratihar
Alfahad Mallick
Debanjan Dutta
Bhaskar Pramanik
Aniruddha Mandal
Mayank Deora
Srinjoy Roy
Subir Paul
Priyobrata Mondal
Sreeya Ghosh
External Advisory Committee
Abhishek Kumar
Avisek Gupta
Bikash Santra
Md. Sahidullah
Sankha Subhra Mullick
Shounak Dutta
Web Chair
Dilip Kumar Gayen
Aniruddha Mandal
External Support Committee
Anal Ray Chowdhury
Arkaprabha Basu
Suchismita Dey
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The Objective: The Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata is organizing the Winter School on Deep Learning: GenAI, LLMs and Beyond. This winter school will focus heavily on developing solutions for from basic to advanced real-world challenging problems with main focus on hands-on sessions, in addition to making the associated theory easy to understand. Participants will learn from the basics of machine learning to the advanced deep learning-based approaches with application to Computer Vision and Natural Language Processing. Theoretical lectures will be delivered by renowned professors and scientists (from ISI and other esteemed organizations) who have made significant contributions in their areas of research. The lectures will be supplemented by extremely detailed hands-on sessions instructed by post-docs and research scholars.
Course coverage: The winter school will have the following course structure (theory and associated hands-on)
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Basics of Python
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Basics of the Deep Learning Library: PyTorch
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Essentials of Matrix Calculus and Linear Algebra for Machine Learning
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Bird’s Eye View of Machine Learning
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Primer on Text, Video and Image Data Processing
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Gradients-based Optimization Techniques
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Rudiments of Artificial Neural Networks and Backpropagation of Error
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Step towards Deep Learning: Activation functions, Normalization techniques, Regularization methods and Loss functions
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Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks and Backpropagation through time (BPTT)
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Attention Models and Transformer (BERT and Visual Transformer)
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Generative Deep Neural Network Models: GANs, VAEs, and Diffusion
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Large Language Models, pre-training, task adaption, and fine-tuning
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Deep Clustering Techniques
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Emerging Learning Strategies: Semi-supervised, Few-shot and Zero-shot
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Deep Reinforcement Learning
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Explainable Artificial Intelligence
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Geometric Deep Learning
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Theory of Deep Learning: Special Focus on GenAI
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Classic Real-world Application (Medical Image Analysis, Image Segmentation, Test Classification, Prompt Engineering, Working with LLMs: Tips and Tricks, quantization, approximation, RLHF, RLAIF etc., Class Imbalanced Learning, Sports Analytics)
Mode of tutorials: Lectures and Hands-on sessions will be conducted in online mode only. All sessions will be on Fridays,Saturdays,Sundays and the recordings will be shared with all the participants.
Who can apply?
Selected applicants will be informed to register for the school.
Important Dates:
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Enrolment of Application |
Nov 22, 2023 – Dec 15, 2023 |
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Registration Opens |
Will be Declared Soon |
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Course Duration |
Jan 12, 2024 – Mar 10, 2024 |
For application, registration fees and other details:
Contacts:
Mobile: (+91) 8240090868 (Arghya)
(+91) 9564746765 (Aniruddha)
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