Neuralhub

Neuralhub is designed to make deep learning experimentation less fragmented. Instead of stitching together local scripts, notebooks and notebooks-hosted GPUs, it puts the whole cycle in one place: build a network from a library of common components, layers and architectures, or start from scratch, then tune and train it in the same interface. Users can also pull in novel research and pre-trained models as starting points.

The workflow is organised into four stages — Build, Tune, Run and Launch. In the tuning stage you construct the network with one click and visually inspect and interact with every component, adjusting hyperparameters such as epochs, features and labels. In the run stage, Neuralhub hosts dedicated machine learning services in the background so training can proceed securely, with access to free compute as well as faster premium GPUs. In the launch stage you review model metrics, test on new datasets, benchmark against other networks, and export, share or publish the result.

The project also positions itself as a community rather than only a tool: published networks can be explored by others, who leave feedback and learn from each other. The site presents Neuralhub as a beta with a waitlist and states that subscribers receive a free premium subscription when it launches, so the platform is still evolving.