Ticking off the boxes: Application criteria
Since we only have a finite number of resources that we can dedicate to this program (after all, we also have to do our jobs ;-), so we want to make sure that both you and we get the most out of the mentorship program. In order for the program to be useful for you and for us, we've set up a few criteria you need to tick off in order to apply for the program.
- You want to learn deep learning. You’re interested in exploring how to apply deep learning (as opposed to machine learning or reinforcement learning) to your idea/project.
- You have an idea or project that is related to your work, whether that's in academia (researcher) or industry doesn't matter.
- You have access to the data you need to complete your project. The data doesn’t have to be ready to be used, but you should have access to it.
- You're willing and able to put in the time necessary. Meaning you have the support from your employer to commit time to work with your Mentor to develop your deep learning skills. The minimum time required is 1h/week with your Mentor + the time you need to study deep learning on your own.
- You are technical in nature. By having a technical background, it will be easier for you to develop your deep learning skills. This is not a hard requirement, but if you don’t have a technical background you would have to dedicate more of your free time to learning deep learning by yourself, otherwise, you will not benefit from the program. You don't need to have an engineering degree or similar, you just need to feel comfortable thinking about numbers and have some idea of the scientific method and/or being data-driven.
Note that all tutoring is going to be done on the Peltarion Platform. There are of course a multitude of deep learning tools, frameworks, libraries, etc. out there and it would be unrealistic for us to say that we have someone that can help you no matter what tool you want to use. Instead, all the tutoring will be done on the Peltarion Platform which allows you to build, train and deploy deep learning models right from your browser, no-code required. It’s the easiest way to focus on problem-solving and not the tedious and oh so boring hardware setup work, learning and stringing together a patchwork of tools or debugging code.