Introduction to Deep Learning by Sandro Skansi

Author:Sandro Skansi
Language: eng
Format: epub, pdf
ISBN: 9783319730042
Publisher: Springer International Publishing

The number of neurons in the hidden layer

The number of neurons in the output layer

Initial values for weights

Initial values for biases

Note that the neurons are not objects. They exist as entries in a matrix, and as such, their number is necessary for specifying the matrices. The weights and biases play a crucial role: the whole point of a neural network is to find a good set of weights and biases, and this is done through training via backpropagation, which is the reverse of a forward pass. The idea is to measure the error the network makes when classifying and then modify the weight so that this error becomes very small. The remainder of this chapter will be devoted to backpropagation, but as this is the most important subject in deep learning, we will introduce it slowly and with numerous examples.

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