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.
DataVec examples
Most of the examples make use of DataVec, a toolkit for preprocessing and clearing 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. If you're unfamiliar with DataVec, here is a description and some links to useful examples.
IrisAnalysis.java
This example takes the canonical Iris dataset of the flower species of the same name, whose relevant measurements are sepal length, sepal width, petal length and petal width. It builds a Spark RDD from the relatively small dataset and runs an analysis against it.
This example loads data into a Spark RDD. All DataVec transform operations use Spark RDDs. Here, we use DataVec to filter data, apply time transformations and remove columns.
DataMeta data tracking - i.e. seeing where data for each example comes from - is useful when tracking down malformed data that causes errors and other issues. This example demonstrates the functionality in the RecordMetaData class.
To build a neural net, you will use either MultiLayerNetwork or ComputationGraph. Both options work using a Builder interface. A few highlights from the examples are described below.
MNIST dataset of handwritten digits
MNIST is the "Hello World" of deep learning. Simple, straightforward, and focused on image recognition, a task that Neural Networks do well.
MLPMnistSingleLayerExample.java
This is a Single Layer Perceptron for recognizing digits. Note that this pulls the images from a binary package containing the dataset, a rather special case for data ingestion.
Data flows through feed-forward neural networks in a single pass from input via hidden layers to output.
These networks can be used for a wide range of tasks depending on they are configured. Along with image classification over MNIST data, this directory has examples demonstrating regression, classification, and anomaly detection.
Training a network over a large volume of training data takes time. Fortunately, you can save a trained model and load the model for later training or inference.
SaveLoadComputationGraph.java
This demonstrates saving and loading a network build using the class ComputationGraph.
t-Distributed Stochastic Neighbor Embedding (t-SNE) is useful for data visualization. We include an example in the NLP section since word similarity visualization is a common use.
How do autonomous vehicles distinguish between a pedestrian, a stop sign and a green light? A complex neural net using Convolutional and Recurrent layers is trained on a set of training videos. The trained network is passed live onboard video and decisions based on object detection from the Neural Net determine the vehicles actions.
This example is similar, but simplified. It combines convolutional, max pooling, dense (feed forward) and recurrent (LSTM) layers to classify frames in a video.
ND4J is a tensor processing library. It can be thought of as Numpy for the JVM. Neural Networks work by processing and updating MultiDimensional arrays of numeric values. In a typical Neural Net application you use DataVec to ingest and convert the data to numeric. Classes used would be RecordReader. Once you need to pass data into a Neural Network, you typically use RecordReaderDataSetIterator. RecordReaderDataSetIterator returns a DataSet object. DataSet consists of an NDArray of the input features and an NDArray of the labels.
The learning algorithms and loss functions are executed as ND4J operations.
Basic ND4J examples
This is a directory with examples for creating and manipulating NDArrays.
Deep learning algorithms have learned to play Space Invaders and Doom using reinforcement learning. DeepLearning4J/RL4J examples of Reinforcement Learning are available here:
