Neural Architecture Search (NAS) Tutorial

This tutorial will provide an overview of Neural Architecture Search (NAS), a crucial technique in the field of deep learning for automatically discovering the best neural network architectures for a given task.

What is Neural Architecture Search?

NAS is a process of automatically discovering the best neural network architectures for a specific task. Instead of manually designing architectures, NAS algorithms explore a large space of possible architectures to find the one that performs best on the given task.

Why is NAS important?

Efficiency: NAS can help in finding efficient architectures that require fewer computations and less memory.
Performance: NAS can lead to architectures that outperform manually designed architectures in terms of accuracy and speed.
Automation: NAS automates the process of designing neural networks, reducing the need for human expertise.

NAS Workflow

Define the search space: Define the set of operations, layers, and parameters that can be used to create the neural network.
Define the objective function: Define the metric to evaluate the performance of the neural network, such as accuracy, FLOPs, or inference time.
Search for the best architecture: Use a search algorithm to explore the search space and find the best architecture.
Train and evaluate the best architecture: Train the best architecture on the dataset and evaluate its performance.

Types of NAS Algorithms

Evolutionary Algorithms: Use principles of natural selection and genetics to evolve the best architecture.
Bayesian Optimization: Use probabilistic models to guide the search process.
** reinforcement learning**: Use reinforcement learning to train the algorithm to search for the best architecture.

Example: NASNet

NASNet is a state-of-the-art architecture discovered using the NAS algorithm. It has been used in various tasks, including image classification and object detection.