EC461N Advanced Deep Learning and Applications
Course Name:
EC461N Advanced Deep Learning and Applications
Programme:
B.Tech (ECE)
Category:
Programme Specific Electives (PSE)
Credits (L-T-P):
(2-0-2) 3
Content:
Review of CNNs for classification and segmentation tasks, Advance concepts-depth-wise separable convolution, Atrous convolution, Group Convolution, Gated Atrous Pyramid Pooling, Attention and Selfattentions. Deep CNN Models for Regression, classification and Segmentations task: ResNet Models, Variants of UNet, BiSeNet V2. Advance CNNs for Object Detection and Text Classification, Graph Neural Networks. Vision Transformer and its Applications: object segmentation, detection and classification.
References:
IanGoodfellow, YoshuaBengio, and Aaron Courville Deep Learning, MIT Press, 2016.
Mahmoud Hassaballah and Ali Ismail Awad, Deep Learning in Computer Vision: Principles and Applications, CRC Press, 2020.
Rowel Atienza, Advance deep learning with Keras, Packt, October 2018
Wang,X.,Zhao, Y.&Pourpanah, F.Recent advances in deep learning. Int.J.Mach. Learn. & Cyber.11,747-750 (2020).https://doi.org/10.1007/s13042-020-01096-5
Alexey Dosovitskiy, et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, https://arxiv.org/abs/2010.11929
Xiangning Chen, Cho-Jui Hsieh, Boqing Gong, “When Vision Transformers Outperform ResNets Without Pre-Training or Strong Data Augmentations”,2022
Department:
Electronics and Communication Engineering(ECE)