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Forwarded from Artificial Intelligence && Deep Learning (SHOHRUH)
MIT 6.S191 Introduction to Deep Learning 2021

Course
Description MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!
March 2, 2021
ERFNet: Efficient Residual Factorized ConvNet for
Real-time Semantic Segmentation [Cited by 452]

paper:
http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf

github [PyTorch]:
https://github.com/Eromera/erfnet_pytorch
April 6, 2021