Guidelines for success
When it comes to DL, we can only truly grapple with its performance. Even from a performance perspective, a lot of DL projects fail to give the results their own engineers are hoping for when everything goes “right,” so it’s important to manage expectations across functions. This includes managing the expectations of your leadership team as well. If you’re an AI PM or entrepreneur and you’re thinking of incorporating DL, do so in the spirit of science and curiosity. Remain open about your expectations.
Make sure you’re setting your team up for success by focusing on data quality as well. A big part of your ANN’s performance lies in the data preparation you take before you start pre-training your models. Passing your data through an ANN is the last step in your pipeline. If you don’t have good validation or if the quality of your data is poor, you’re not going to see positive results. Then, once...
