Convolutional Neural Networks (CNNs) | Deep Learning

CNNs are a go-to deep learning architecture for many computer vision tasks, from image classification to object detection and more. Here, we take a look at the basics, and see how they use biologically-inspired hierarchical feature extraction to do what they do.

Timestamps
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Introduction 00:00
Kernel convolutions 00:41
Common kernels 02:30
Why flipping? 03:30
Convolution as feature extraction 04:00
Hierarchical feature extraction 05:40
Down-sizing 07:25
Max-pooling 09:43
Multi-channel kernels 10:30
Learnable kernels 11:04
CNN architecture 11:52
Residual connections 14:25
Convolution vs. cross-correlation 15:55

Useful links
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ResNet paper: https://arxiv.org/abs/1512.03385
CNN tutorial in TensorFlow: https://www.tensorflow.org/tutorials/...
Gradient calculation in convolutional layers: https://deeplearning.cs.cmu.edu/F21/d... and https://sites.cc.gatech.edu/classes/A...