Fabian Ulmer

For our company, we chose Git as version control software and Guthib to host our portfolio via Internet to our team. We are interested in exploring how much impact Github can have on our development besides contolling our portfolio changes.

My company needed to communicate effectively and orderly. For this collaboration, I chose Slack because it reduces the overall messages send with the option to add reactions. I can further notify my team members and set reminders for them and myself as well as integrate up to 10 helpful tools such as quick voting to support communication.

I chose TensorFlow.js for my company to make our Machine Learning models created by Keras run on our a JavaScript Server. I hope this works out - will give an update in the following weeks.

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

I chose Python as it helps my company to perform data analyses on .csv data. As a general purpose programming language, it can further help us in solving and connecting our use cases to generate information out of IMU data. For solving our use cases, my companies' additional tech choices use Python as a high-level language base. As one of the biggest programming languages, Python has a large community and is generally seen as easy to read which makes it is accessible to our devs that do not have knowledge in Python.