Deep Learning Course (NYU, Spring 2020) - Week 3 – Practicum: Natural signals properties and CNNs

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42.5 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

Deep Learning Yann LeCun NYU PyTorch convolutional neural networks CNN ConvNet locality stationarity compositionality sparsity parameter sharing

Full Transcript

Course website: http://bit.ly/DLSP20-web Playlist: http://bit.ly/pDL-YouTube Speaker: Alfredo Canziani Week 3: http://bit.ly/DLSP20-03 0:00:00 – Week 3 – Practicum PRACTICUM: http://bit.ly/DLSP20-03-3 Properties of signals that are most relevant to CNNs are discussed, namely:- Locality, Stationarity, and Compositionality. How a kernel exploits these features by using Sparsity, Weight sharing and Stacking of layers is explored next, along with the concepts of padding and pooling. A performance comparison between FCN and CNN for different data modalities was also made. 0:00:26 – Properties of natural signals 0:17:54 – Exploiting Properties of Natural Signals to Build Standard Spatial CNN 0:39:36 – Pooling and Covnet - Jupyter Notebook

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