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(Convolutional) Neural network tutorial - BigMax workshop - Dresden, April 2019

Authors: Angelo Ziletti, Andreas Leitherer, and Luca M. Ghiringhelli - Fritz Haber Institute of the Max Planck Society, Berlin

In this tutorial, we briefly introduce the main ideas behind convolutional neural networks, build a neural network model, and finally explain the classification decision process using attentive response maps.

(Convolutional) Neural network tutorial - BigMax workshop - Dresden, April 2019

Authors: Angelo Ziletti, Andreas Leitherer, and Luca M. Ghiringhelli - Fritz Haber Institute of the Max Planck Society, Berlin

In this tutorial, we briefly introduce the main ideas behind convolutional neural networks, build a neural network model with Keras, and explain the classification decision process using attentive response maps.

... ... @@ -11991,10 +11991,10 @@ Convolutional networks have been tremendously successful in practical applicatio

The name "convolutional neural network" indicates that the network employs a mathematical operation called convolution. Convolution is a specialized kind of linear operation.

A typical layer of a convolutional network consists of three stages:

1. Convolution stage: the layer performs several convolutions in parallel to produce a set of linear activations.

2. Convolution stage: the layer performs several convolutions in parallel to produce a set of linear activations (see Sec. 3 for more details).

3. Detector stage: each linear activation is run through a nonlinear activation function (e.g. rectified linear activation function)

activation function, sigmoid or tanh function)

4. Pooling stage: a pooling function is used to modify (downsample) the output of the layer. A pooling function replaces the output of the network at a certain location with a summary statistic of the nearby outputs. For example, the max pooling operation reports the maximum output within a rectangular neighborhood. Other popular pooling functions include the average of a rectangular neighborhood, the \$L^2\$ norm of a rectangular neighborhood, or a weighted average based on the distance from the central pixel.