How to Know if a Neural Network is Right for Your Machine Learning Initiative - KDnuggets

Deep learning models (aka neural nets) now power everything from self-driving cars to video recommendations on a YouTube feed, having grown very popular over the last couple of years. Despite their popularity, the technology is known to have some drawbacks, such as the deep learning "reproducibility crisis"-- as it is very common for researchers at one to be unable to recreate a set of results published by another, even on the same data set. Additionally, the steep costs of deep learning would give any company pause, as the FAANG companies have spent over $30,000 to train just a single (very) deep net. Even the largest tech companies on the planet struggle with the scale, depth, and complexity of venturing into neural nets, while the same problems are even more pronounced for smaller data science organizations as neural nets can be both time-and cost-prohibitive. Also, there is no guarantee that neural nets will be able to outperform benchmark models like logistic regression or gradient-boosted ones, as neural nets are finicky and typically require added data and engineering complexities.