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Each of them is connect to all neurons of the previous layer and to all. Neurons of the next layer and each connection has its own numerical weight associate with it. The initial data vector of features arrive at the input layer from where they are transferr. To the neurons of the first inner or hidden layer; the transmits value is multiplid by the link weight. The hidden layer neuron sums up the signals arriving at its input. Applies the so-calld activation function usually non-linear to the sum Fig. Activation functions are usd differently the graphs of some of them are shown below. Fig _activation Examples of different activation functions Sometimes before applying the activation function a fixd bias is addd to the sum of the input signals; each layer has its own offset value.

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The resulting value is passd along the connections to the neurons of the next layer and so on until the output layer is reached. The output layer is arrangd similarly to the hidden ones. The values ​​calculatd in the neurons of the output layer are the result of the model. The parameters of the model are the weights on the links and the values ​​of the Belgium Email List displacements. Training the model respectively comes down to fitting the weights and biases. As a rule the backpropagation method is usd for training which is a special case of the general model training method describd above. It allows you to sequentially move in the direction of decreasing the error of the weight of each layer starting from the output.

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Convolutional and recurrent networks Multilayer perceptrons are well suitd for problems in which the feature vector has a fixd length that is not very large but this condition is not always met. Consider for BTC Email List example the problem of classifying raster images. Let’s say we want to train a model that distinguishes between dogs and cats in photographs. The input vector here is the picture itself consisting of at least tens of thousands of pixels.

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