Build AI visually. Run it in pure Java.
Deep Netts is a deep learning development platform for the JVM: a visual AI builder that takes you from data to trained model, and a high-performance pure Java library that runs that model inside your applications.
Two components. One workflow.
Build and train models in the Visual AI Builder, then embed them in production with the Deep Learning Java Library. Same models, same platform, no handoff between stacks.
Visual AI Builder
A graphical development environment for deep learning. Import data, design the network with drag-and-drop or let a wizard propose one, train, and watch every metric as it happens.
Deep Learning Java Library
A high-performance, pure Java implementation of deep learning. Train and run models with a fluent API, accelerated through the Vector API on CPUs and CUDA on GPUs.
// Build a network with the fluent Java API
var neuralNet = FeedForwardNetwork.builder()
.addInputLayer(numInputs)
.addFullyConnectedLayer(32, ActivationType.RELU)
.addOutputLayer(1, ActivationType.SIGMOID)
.lossFunction(LossType.CROSS_ENTROPY)
.build();
// Train on your data
neuralNet.train(trainingSet);
// Use it like any other Java object
float risk = neuralNet.predict(transaction);What you can build with it
Regression & Approximation
Predict continuous values – forecasts, estimates, sensor readings.
Classification
Sort inputs into categories – fraud or legitimate, churn or retain.
Image Recognition
Recognize and classify images with convolutional networks.
Text Classification
Categorize documents, tickets, and messages by content.
Everything between raw data and a running model.
Step-by-step wizards
Go from dataset to trained model through guided steps – no boilerplate to get started.
Visual model builder
Design network architectures with drag-and-drop, then inspect them layer by layer.
End-to-end training & debugging
Watch training live, analyze errors, and understand model behavior before you ship it.
Feedforward & convolutional networks
The architectures that cover most business problems, tuned for the JVM.
TensorFlow compatibility
Exchange models with the wider ML ecosystem instead of being locked in.
Direct Java integration
Trained models are Java objects – call them from your code like any other class.
Want to see it applied? Explore production use cases or download the free edition.
Bring high-performance AI into your Java architecture.
Evaluate Deep Netts on your workload. See what Java-native AI can do.