© Umberto Michelucci 2018
Umberto MichelucciApplied Deep Learninghttps://doi.org/10.1007/978-1-4842-3790-8_8

8. Convolutional and Recurrent Neural Networks

Umberto Michelucci1 
(1)
toelt.ai, Dübendorf, Switzerland
 

In the previous chapters, you have looked at fully connected networks and all the problems encountered while training them. The network architecture we have used, one in which each neuron in a layer is connected to all neurons in the previous and following layer, is not really good at many fundamental tasks, such as image recognition, speech recognition, time series prediction, and many more. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are the advanced architectures most often used today. In this chapter, you ...

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