Convnets

Prof. Eric A. Suess

Chapter 8 covers

• Understanding convolutional neural networks (convnets)
• Using data augmentation to mitigate overfitting
• Using a pretrained convnet to do feature extraction
• Fine-tuning a pretrained convnet
• Visualizing what convnets learn and how they make classification decisions

Computer Vision

Convolutional neural networks revolutionized the field of computer vision and are now the main type of deep learning neural networks that are used in this feild.

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kaggle ImageNet

ImageNet

MNIST again

• Eariler, using a feed forward neural network, the accuracy was 97.8%
• Now, using a convolutional neural network, the accuracy can be improved, beyond 99%.

Convnets work locally

What is the difference between a densely connected layer and a convolution layer?

• Dense layers learn global patterns in their input feature space
• Convolution layers learn local patterns

Convnets have an interesting property

• The patterns they learn are translation invariant.
• They can learn spatial hierarchies of patterns.