End-to-end learnable discrete-continuous models are compositional, tend togeneralize better,and are more interpretable.

MultiLayerNetwork'MultiLayerNetwork' consists of a single input layer and a single output layer with a stack of layers in between them.

This page describes how to build more complicated networks, using DL4J's Computation Graph functionality. New Features!!! as of 7.3 Computation Graph will support a parameterless LossLayer and vertices for performing triplet loss. As a general rule, when building networks with a single input layer, a single output layer, and an input- a- b- c- output type connection structure: MultiLayerNetwork is usually the preferred network. However, everything that MultiLayerNetwork can do, ComputationGraph can do as well - though the configuration may be a little more complicated. The basic idea is that in the ComputationGraph, the core building block is the GraphVertex, instead of layers.

Deeplearning4j's Github repository has many examples to cover its functionality. The Quick Start Guide shows you how to set up Intellij and clone the repository. This page provides an overview of some of those examples. Most of the examples make use of DataVec, a toolkit for preprocessing and clearning data through normalization, standardization, search and replace, column shuffles and vectorization. Reading raw data and transforming it into a DataSet object for your Neural Network is often the first step toward training that network.