getting started with neural networks anatomy of neural network as you saw in the previous training neural network revolves around the following objectslayerswhich are combined into network (or modelthe input data and corresponding targets the loss functionwhich defines the feedback signal used for learning the optimizerwhich determines how learning proceeds you can visualize their interaction as illustrated in figure the networkcomposed of layers that are chained togethermaps the input data to predictions the loss function then compares these predictions to the targetsproducing loss valuea measure of how well the network' predictions match what was expected the optimizer uses this loss value to update the network' weights input weights layer (data transformationweights layer (data transformationweight update optimizer predictions ytrue targets loss function loss score figure relationship bet ween he net worklayersloss functionand optimizer let' take closer look at layersnetworksloss functionsand optimizers layersthe building blocks of deep learning the fundamental data structure in neural networks is the layerto which you were introduced in layer is data-processing module that takes as input one or more tensors and that outputs one or more tensors some layers are statelessbut more frequently layers have statethe layer' weightsone or several tensors learned with stochastic gradient descentwhich together contain the network' knowledge different layers are appropriate for different tensor formats and different types of data processing for instancesimple vector datastored in tensors of shape (samplesfeatures)is often processed by densely connected layersalso called fully connected or dense layers (the dense class in kerassequence datastored in tensors of shape (samplestimestepsfeatures)is typically processed by recurrent layers such as an lstm layer image datastored in tensorsis usually processed by convolution layers (conv dlicensed to