Deep Netts Platform
Accelerates development, enables scalable deployment and offers privacy and security within the Java native environment.
What is Deep Netts platform?
Deep Netts is a deep learning development platform in Java. We provide everything you need to build, test and deploy machine learning models in Java. By utilizing visual tools and expert wizards to bridge the gap between software and AI development, the platform simplifies the development process, enhances productivity, and improves accuracy and understanding of machine learning (ML) models. Pure Java software library substantially simplifies integration into existing Java applications and deployment of ML models on Java virtual machines.
What can you do
with Deep Netts platform?
Build Model
What Can You Do
Deploy Model
What Can You Do
Debug and Understand The Model
What Can You Do
Advanced Machine Learning Algorithms
Approximation
Model outputs as function of inputs.
Classification
Assign categories to given patterns.
Image Recognition
Recognize images and automate visual workflows.
Text Classification
Assign a category to open-ended text.
What's Included?
Visual AI Builder
ML Development tool with step by step visual expert guide. Integrated environment for entire ML development process.
Deep Learning Java Library
High performance, low latency deep learning Java library for embedding ML models into existing applications.
//create an instance of a neural network using builder
FeedForwardNetwork neuralNet = FeedForwardNetwork.builder()
.addInputLayer(numInputs)
.addFullyConnectedLayer(1024, ActivationType.RELU)
.addOutputLayer(numInputs, ActivationType.SIGMOID) .lossFunction(LossType.CROSS_ENTROPY)
.build();
//set training settings
neuralNet.getTrainer().setStopError(0.02f)
.setStopEpochs(300)
.setLearningRate(0.001f);
//run training to build the model
neuralNet.train(dataSet);
Features Highlights
Discover the power and innovation behind our platform.
Deep Netts Platform Features
Deep Netts Java library enables simplified use and deployment of ML models in Java-native environments.
Step by step model wizard
A user-friendly step-by-step wizard guides you throughout the entire process of building models. Even if you’re not a ML expert and you don’t fully understand all of the ML magic, you’ll quickly get the initial working version of a model for your project by answering wizard’s simple questions.
Visual model builder
Enables you to modify model architecture by adding or re-configuring layers, using an intuitive drag-n-drop tool and properties editor. Builder also provides commonly used default settings customized specifically for your project and prevent you from using architectures and setting that doesn’t make sense.
Understanding ML models
Gain insights into model inner workings, including architecture, parameters, and training process, to comprehend how they make predictions and evaluate their performance. Understanding how specific architecture and training settings influence a model predictions, and why model gives certain results is the key for improving and maintaining models.
Tensorflow support
Importing trained Tensorflow models and running them on Deep Netts them in Java native environments with minimum set of external dependencies greatly improves stability, portability, cost effectiveness and overall security.
End-to-end model training and debugging
End-to-end model training and testing process enables you to quickly iterate through various training settings, identify and resolve issues of the models. This greatly accelerates the model building and enables building better models faster.
Feed forward and Convolutional Neural Networks
Out-of-the-box support for the most commonly used machine learning models for image recognition, classification and numeric prediction. These models can be customized for wide range of machine tasks, and type of data. They represent base on which all advanced types of models can be built.
Visual model analysis
Examine and interpret models through the use of visualizations which enable a deeper understanding of the model’s behavior and the underlying patterns in data. Weather there is an issue data, with model architecture or training procedure, various visual tools enable you to quickly run diagnostics and identify them.
Build and use models directly in Java code
Clean integration of trained machine learning models into your application using intuitive and readable API based on official Java technology standard (JSR381).
Visual AI Builder
A graphical, drag‑and‑drop interface featuring a step‑by‑step wizard for creating, training, and debugging ML models visually
