The dataset that I found is called NYC Taxis: A Day in the Life, which analyzes the travel path of individual taxis operating in NYC. While a bit more niche in applicability than some of the other datasets, this specific dataset could be utilized in a variety of ways. The New York City Transit Authority (NYCTA), for example, could use the data to cross-reference the paths of its own buses and trains to best determine which areas of NYC remain underserved in terms of transportation methods. Moreover, companies like Uber and Lyft can use it to determine where gaps in the market for personal transport remain, and which areas provide the steepest competition from NYC taxi services.
I found the book Neural Networks and Deep Learning quite interesting for a variety of reasons. On a personal level, I minored in psychology during my undergrad and am relatively familiar with the anatomy and functions of the brain, so replicating that through advanced programming is something I find fascinating. Moreover, while neural networks have only recently risen in prominence, they will likely begin to be integrated into several major non-STEM sectors.