Overview of Neural Architecture Search Paperspace Blog

The hyperparameter optimization problem has been solved in many different ways for classical machine learning algorithms. Some examples include the use of grid search, random search, Bayesian optimization, meta-learning, and so on. But when considering deep learning architectures, the problem becomes much harder to deal with. In this article we will cover the problem of neural architecture search and the current state of the art. This article assumes a basic knowledge of different neural networks and deep learning architectures. This is Part 1 of a series which will take you through what the problem of neural architecture search (NAS) is, and how to implement various interesting approaches for NAS using Keras. Deep learning engineers are expected to have an intuitive understanding of what architecture might work best for what situation, but this is rarely the case. The possible architectures one can create are endless.