Delve deep into the world of neural architecture search to understand how it is helping in object identification
Neural architecture search is currently an emergent area. A lot of research is going on and there are many different approaches to the task. There isn’t a single best method generally or even a single best method for a specialized kind of problem such as object identification in images. Neural architecture search is an aspect of AutoML, along with feature engineering, transfer learning, and hyperparameter optimization. It’s probably the hardest machine learning problem currently under active research; even the evaluation of neural architecture search methods is hard. Neural architecture search research can also be expensive and time-consuming. The metric for the search and training time is often given in GPU-days, sometimes thousands of GPU-days.
