Course Details
Subject {L-T-P / C} : CS6510 : Deep Learning { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Puneet Kumar Jain
Syllabus
Module 1 : |
UNIT – I
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Course Objective
1 . |
Understand the fundamentals of feedforward, recurrent and convolutional neural networks and apply them to solve various real-world problems. |
2 . |
Develop a deeper understanding in different optimization algorithms gradient descent, stochastic gradient descent and recent developments like ADAM and RMSprop. |
3 . |
Explore state-of-the-art architectures (CNN, LSTM, GRU, Bidirectional Models and Deep Generative Models) and learn transfer learning with feature and decision level fusion. |
4 . |
Learn to improve the computational efficiency and performance of DL models for a specific problem by optimizing hyperparameters and employing GPUs |
Course Outcome
1 . |
1. Develop in depth understanding of the key deep learning models and concepts.
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Essential Reading
1 . |
A. Zhang, Z.C. Lipton, M. Li, A.J. Smola, Dive into Deep Learning, Cambridge University Press , Website: https://d2l.ai |
2 . |
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press , Website: https://www.deeplearningbook.org/ |
Supplementary Reading
1 . |
Francois Chollet, Deep Learning with Python, Manning Publishers |
2 . |
E. Stevens, L. Antiga, T. Viehmann, Deep Learning with PyTorch, Manning Publishers |
Journal and Conferences
1 . |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
2 . |
AAAI Conference on Artificial Intelligence |