Deeplearning4j, or DL4J, is a deep learning library written in Java. It features a distributed as well as a single-machine deep learning framework that includes and supports various neural network structures such as feedforward neural networks, RBM, convolutional neural nets, deep belief networks, autoencoders, and others. DL4J can solve distinct problems, such as identifying faces, voices, spam, or e-commerce fraud.

Deeplearning4j is also distributed under the Apache 2.0 license and can be downloaded from http://deeplearning4j.org. The library is organized as follows:

• org.deeplearning4j.base: These are loading classes
• org.deeplearning4j.berkeley: These are math utility methods
• org.deeplearning4j.clustering: This is the implementation of k-means clustering
• org.deeplearning4j.datasets: This is dataset manipulation, including import, creation, iterating, and so on
• org.deeplearning4j.distributions: These are utility methods for distributions
• org.deeplearning4j.eval: These are evaluation classes, including the confusion matrix
• org.deeplearning4j.exceptions: This implements the exception handlers
• org.deeplearning4j.models: These are supervised learning algorithms, including deep belief networks, stacked autoencoders, stacked denoising autoencoders, and RBM
• org.deeplearning4j.nn: These are the implementations of components and algorithms based on neural networks, such as neural networks, multi-layer networks, convolutional multi-layer networks, and so on
• org.deeplearning4j.optimize: These are neural net optimization algorithms, including back propagation, multi-layer optimization, output layer optimization, and so on
• org.deeplearning4j.plot: These are various methods for rendering data
• org.deeplearning4j.rng: This is a random data generator
• org.deeplearning4j.util: These are helper and utility methods