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Introduction:

Hyperparameter Optimization for Neural Architecture Search is a critical area of research in machine learning and artificial intelligence. Neural architecture search aims to automatically generate the optimal architecture of a neural network for a specific task, while hyperparameter optimization focuses on finding the best set of hyperparameters for training the neural network. In this thesis, we will delve into the current methods and techniques used for hyperparameter optimization in neural architecture search, and explore new approaches to improve the efficiency and effectiveness of this process.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objective of Study
1.4 Limitation of Study
1.5 Scope of Study

Chapter 2: Literature Review
2.1 Neural Architecture Search
2.2 Hyperparameter Optimization
2.3 Current Methods and Techniques
2.4 Challenges and Issues

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Experiment Design
3.3 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Performance Comparison
4.2 Analysis of Results
4.3 Implications for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Recommendations for Future Work

Thesis Overview:

The field of machine learning has seen rapid advancements in recent years, especially in the areas of neural architecture search and hyperparameter optimization. This thesis focuses on exploring the current methods and techniques used for hyperparameter optimization in neural architecture search, and proposes new approaches to improve the efficiency and effectiveness of this process. By conducting experiments and analyzing the results, the goal is to provide valuable insights that can help researchers and practitioners in the field of machine learning to enhance the performance of neural networks for various tasks. The findings of this thesis have the potential to contribute significantly to the ongoing research efforts in the field of artificial intelligence and machine learning.

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