Inspiration
As software engineers, we have all known what it’s like to start from square one in our learning, especially when it comes to newer technologies constantly being developed around us like artificial intelligence. With concepts such as neural networks and machine learning becoming increasingly intimidating to tackle in modern day, we sought to take inspiration from simplistic user applications like Scratch that helped us learn to code with ordinary drag and drop mechanics! Optml provide a friendly, easy to use interface to build neural networks while getting introduced to the statistical concepts that make this technology possible.
What it does
Optml allows the user to graphically design and tune their own machine learning model, learning what important terms mean, and more importantly, what they do in the process! The user can drag and drop to connect different types of nodes together to represent the different types of layers that can be utilized in a sequential model. Ranging from a simple perceptron, to AlexNet, to 3D convolutions, Optml already provides a wide range of network types to build. Once the user is satisfied with their model design, they can feed their training data and observe the results. Metrics will appear to document the accuracy and loss of the model as it undergoes training for a certain number of epochs. Furthermore, they can alter and perfect their layers and hyperparameters to get the best performing model they can.
For those trying to develop a use case, a download button is available to download the full h5 model with the trained weights. .h5 files can be easily used in keras based machine learning frameworks like tensorflow or huggingface, but can also be imported into other frameworks.