AutoML with Auto-sklearn培訓

Introduction

Setting up a Working Environment

Installing Auto-sklearn

Anatomy of a Standard Machine Learning Workflow

How Auto-sklearn Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-sklearn

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-sklearn for Deep Learning Models

Troubleshooting

Summary and Conclusion