DLHUB is a graphical deep learning platform that enables deep learning design without requiring any programming skills. User can develop, train, and evaluate deep learning models to tackle real-life industrial solutions by merely drag-and-drop technique and utilizing the model verification feature. It is ideal for engineers who do not have a profound knowledge of deep learning and Python or C# programming skills to properly design a deep learning neural network with the desired architecture. User can effortlessly develop the deep learning model in DLHUB and integrate it into any applications with minimal API calls.

Equipped with a user-friendly graphical interface and model verification feature, DLHUB allows users to complete any deep learning design tasks with ease. Users can construct any deep learning architecture by graphically adding and configuring appropriate deep learning layers into this architecture. DLHUB supports various deep learning layer types from core layers such as a fully connected layer (Dense), convolutional layer (Con2D), pooling layer (Pool2D) to advanced layers that include transfer learning model (TLModel), and residual neural network layer (ResNet).

DLHUB supports advanced training algorithms, including AdaDelta, AdaGrad, Adam, FSAdaGrad, RMSProp, SGD, and Momentum SDG that help to improve the convergence rate. During the training process, a graphics processing unit (GPU) will be detected and enable automatically to speeding up training speed. Mini-batch training technique is also supported to handle big data problem. DLHUB also supports a built-in graphical test interface to evaluate and test the trained deep learning model on an unseen test data before deployment.