In this chapter, we examined deep learning techniques for neural networks. All API support in this chapter was provided by Deeplearning4j. We began by demonstrating how to acquire and prepare data for use with deep learning networks. We discussed how to configure and build a model. This was followed by an explanation of how to train and test a model by splitting the dataset into training and testing segments.

Our discussion continued with an examination of deep learning and regression analysis. We showed how to prepare the data and class, build the model, and evaluate the model. We used sample data and displayed output statistics to demonstrate the relative effectiveness of our model.

RBM and DBNs were then examined. DBNs are comprised of RBMs stacked together and are especially useful for classification and clustering applications. Deep autoencoders are also built using RBMs, with two symmetrical DBNs. The autoencoders are especially useful for feature selection and extraction.

Finally, we demonstrated a convolutional network. This network is modeled after the visual cortex and allows the network to use regions of information for classification. As in previous examples, we built, trained, and then evaluated the model for effectiveness. We then concluded the chapter with a brief introduction to recurrent neural networks.

We will expand upon these topics as we move into next chapter and examine text analysis techniques.