I'm going to summarize everything I learned over the first week. I'll write this section based on what I comprehended during lectures, rather than what I read outside of lecture.
First and foremost, it was apparent from the start that we needed to pick a topic. Although selecting a topic appears to be a simple task, I discovered that there are other factors to consider. Before you begin, make sure you have the following information. Before we go through these stages, we need to answer certain basic, yet innovative, and essential questions developed by DARPA, and these questions are a method of achieving our objectives.
In addition, I knew that we would conduct study, which is more than just theory. At the end of the day, we must have a working system to give over, along with the documentation document.
When it came to picking a topic, I discovered that the most important factor to consider was data accessibility. If we want to develop a system that identifies lung cancer, for example, we should use pictures of healthy and malignant lungs that are freely available on the Internet. Because obtaining that data necessitates the utilization of time, authoritative individuals, and resources.
As a result, it is important to examine trustworthy sources, such as Kaggle contests themselves, before deciding on a topic.
I recall the professor saying that documentation is highly essential and that using diagrams within is recommended. Finally, while selecting a topic, as I learnt here, it is critical to go through each step carefully and ensure that our responses match the criteria we set for ourselves.