Automated Machine Learning

Download free course Automated Machine Learning, pdf file on 223 pages by by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.

This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Table of contents

Pages :	223
Size :	6.4 MB
File type :	PDF
Downloads:	71
Created:	2022-02-01
License:	CC BY
Author(s):	by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren

Others machine learning Tutorials

Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers

Interpretable Machine Learning

Overview of Machine Learning

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

Understanding Machine Learning

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