The Modeling view lets you build a deep learning model yourself or work with a colleague on the same model. There are no limits to how you can build the model - it’s as easy to make a simple one as to make a complex, multi-layered one.
There is no coding needed, just drag and drop to build your model. No coding means it’s simpler and faster but also fewer bugs.
In the Modeling canvas you’ll find everything you need to build a deep learning model in one place. You can drill down into an experiment, but there is no dense information that is hard to digest. Only the relevant information is seen.
You’ll see your model on the canvas and all settings on the side, with Run settings, Dataset settings, and all building blocks that you need to build an AI model as well as pre-built deep learning architectures called pretrained blocks.
You can switch between experiments in the drop-down or go back to the Experiments overview by clicking the Experiments overview button.
The Experiments overview consists of a grid (or table) view where you see all created experiments in the current project.
You can collapse the groups to make the overview less cluttered, which is good if you’ve got a lot of experiments.
On all completed and running experiments, you can see a loss graph. Look at it to check if the curve converges properly. The graph also makes it easy to compare eperiments quickly.
Filter out experiments
You can filter out experiments you want to see based on:
Sort your experiments
You can sort your experiments by their status or time created.