Deep Learning Course (NYU, Spring 2020) - Week 5 – Practicum: 1D multi-channel convolution and autograd

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42.5 hours of video
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Summary

Keywords

Deep Learning Yann LeCun NYU PyTorch convolutional neural networks CNN ConvNet kernels linear algebra autograd autodiff size

Full Transcript

Course website: http://bit.ly/DLSP20-web Playlist: http://bit.ly/pDL-YouTube Speaker: Alfredo Canziani Week 5: http://bit.ly/DLSP20-05 0:00:00 – Week 5 – Practicum PRACTICUM: http://bit.ly/DLSP20-05-3 We briefly review the matrix-multiplications and then discuss the convolutions. Key point is we use kernels by stacking and shifting. We first understand the 1D convolution by hand, and then use PyTorch to learn the dimension of kernels and output width in 1D and 2D convolutions examples. Furthermore, we use PyTorch to learn about how automatic gradient works and custom-grads. 0:02:20 – Understanding 1D/2D Convolutions 0:18:15 – Dimension of Kernels and Output Width in PyTorch 0:27:14 – How Automatic Gradient Works?

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