A Refresher Guide to Convolution Neural Networks
This book is 100% complete
Completed on 2020-03-27
About the Book
The goal of any Convolution Neural Network is to learn higher-order features from data representation, achieving that via convolutions. This type of Neural Nets is very good in dealing with tensor data such as images and is well suited to object recognition with consistently top image classification competitions. In this part, I will try to teach you the convolution neural network on weekend.
An online and free version of this part can be found here.
You can check the Scratching Linear Algebra in Weekend here.
- Table of Content
- CNN Architecture Overview
- Neuron spatial arrangements
- Evolution of the connections between layers
- Input Layers
- Convolution Layers
- Activation maps
- Parameter sharing
- Learned filters and renders
- ReLU activation functions as layers
- Convolution layer hyper-parameters
- Batch normalization and layers
- Pooling Layer
- Fully Connected Layer
- Other Applications of CNNs
- CNNs of Note
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