be aware of validation-set overfitting when turning hyperparametersthe fact that your hyperparameters may end up being overspecialized to the validation set avoiding this is the purpose of having separate test setkey network architectures the three families of network architectures that you should be familiar with are densely connected networksconvolutional networksand recurrent networks each type of network is meant for specific input modalitya network architecture (denseconvolutionalrecurrentencodes assumptions about the structure of the dataa hypothesis space within which the search for good model will proceed whether given architecture will work on given problem depends entirely on the match between the structure of the data and the assumptions of the network architecture these different network types can easily be combined to achieve larger multimodal networksmuch as you combine lego bricks in waydeep-learning layers are lego bricks for information processing here' quick overview of the mapping between input modalities and appropriate network architecturesvector data--densely connected network (dense layersimage data-- convnets sound data (for examplewaveform)--either convnets (preferredor rnns text data--either convnets (preferredor rnns timeseries data--either rnns (preferredor convnets other types of sequence data--either rnns or convnets prefer rnns if data ordering is strongly meaningful (for examplefor timeseriesbut not for textvideo data--either convnets (if you need to capture motion effectsor combination of frame-level convnet for feature extraction followed by either an rnn or convnet to process the resulting sequences volumetric data-- convnets nowlet' quickly review the specificities of each network architecture densely connected networks densely connected network is stack of dense layersmeant to process vector data (batches of vectorssuch networks assume no specific structure in the input featuresthey're called densely connected because the units of dense layer are connected to every other unit the layer attempts to map relationships between any two input featuresthis is unlike convolution layerfor instancewhich only looks at local relationships densely connected networks are most commonly used for categorical data (for examplewhere the input features are lists of attributes)such as the boston housing price dataset used in they're also used as the final classification or regression stage of most networks for instancethe convnets covered in typically end with one or two dense layersand so do the recurrent networks in licensed to