CNN Layers

Four main layers:

Convolutional layer--output neurons that are connected to local regions in the input

 ReLU layer--elementwise activation function

 Pooling layer--perform a downsampling operation along the spatial dimensions

 Fully-connected layer-same as regular neural networks

 Filters act as feature detectors from original image

 Network will learn filters that activate when they see some type of visual features

 ReLu converges much faster than sigmoid/tanh in practice

 Pooling Layer makes representations smaller and more manageable, helps control overfitting

 CNNs have much fewer connections and parameters which are easier to train, traditionally fully-connected neural network is almost impossible to train when initialized randomly

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