Chapter 1, Introduction to Deep Learning in Java, provides a brief introduction to deep learning using DL4J.

Chapter 2, Data Extraction, Transformation, and Loading, discusses the ETL process for handling data for neural networks with the help of examples. 

Chapter 3, Building Deep Neural Networks for Binary Classification, demonstrates how to develop a deep neural network in DL4J in order to solve binary classification problems.

Chapter 4, Building Convolutional Neural Networks, explains how to develop a convolutional neural network in DL4J in order to solve image classification problems.

Chapter 5, Implementing Natural Language Processing, discusses how to develop NLP applications using DL4J.

Chapter 6, Constructing LSTM Networks for Time Series, demonstrates a time series application on a PhysioNet dataset with single-class output using DL4J.

Chapter 7, Constructing LSTM Neural Networks for Sequence Classification, demonstrates a time series application on a UCI synthetic control dataset with multi-class output using DL4J.

Chapter 8, Performing Anomaly Detection on Unsupervised Data, explains how to develop an unsupervised anomaly detection application using DL4J.

Chapter 9, Using RL4J for Reinforcement Learning, explains how to develop a reinforcement learning agent that can learn to play the Malmo game using RL4J.

Chapter 10, Developing Applications in a Distributed Environment, covers how to develop distributed deep learning applications using DL4J.

Chapter 11, Applying Transfer Learning to Network Models, demonstrates how to apply transfer learning to DL4J applications.

Chapter 12, Benchmarking and Neural Network Optimization, discusses various benchmarking approaches and neural network optimization techniques that can be applied to your deep learning application.