Introduction to Vision: Convolutional Neural Networks

Link to Jupyter Notebook: https://github.com/priyammaz/PyTorch-...
Github Repo: https://github.com/priyammaz/PyTorch-...

Today we explore one of the most foundational building blocks of Neural Networks, the Convolution. We will first explore Convolutions in the context of signal processing, and how it enables us to do frequency domain operations in the time/space domain. We also take a look at the Im2Col method (nn.Unfold) to expose the inner workings of convolutions in PyTorch and how we can leverage super efficient Cuda Kernels to accelerate the convolution operation! We will end with implementing the AlexNet Model (and explore all the methods typically found in CNNs) and train again on our Cats vs Dogs dataset!

Timestamps
00:00:00 Introduction
00:01:15 What is a Convolution?
00:34:40 Problems with Linear Layers for Images
00:36:10 Conv2d In PyTorch
00:38:40 Edge Detection with Sobel Filter
00:41:13 Effects of Conv2d Options (Kernel Size, Stride, Padding)
00:46:25 PyTorch Convolutions are Filter Banks
00:59:10 Im2Col (nn.Unfold) to Convert Convolution to Matrix Multiplication
01:18:30 Pooling (Subsampling)
01:23:50 Batch Normalization
01:31:55 Dropout
01:34:57 Typical structure of CNN Models
01:37:00 Implementing AlexNet
01:48:40 Training AlexNet on Dogs vs Cats
02:03:50 Recap

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