Machine Learning: Theory to Applications

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision-making, communication networks, security, and signal processing. It also presents the concept of generative networks and focuses on GANs as a tool to improve the performance of existing algorithms.

Format: Hardback
Length: 202 pages
Publication date: 29 September 2022
Publisher: Taylor & Francis Ltd

The book delves into the fundamental principles of machine learning (ML) while emphasizing its modern applications. Its primary objective is to enhance the understanding of ML by offering technical and practical insights to those seeking to advance their knowledge in this field. Unlike other literature, this work prioritizes presenting the core concepts with a strong emphasis on fresh ideas and real-world application scenarios. It begins by introducing the foundational concepts of ML and gradually expands them to cover various deep learning algorithms. Furthermore, the book provides an introduction to and a comprehensive overview of evaluation tools using Python, guiding readers through recent ML applications in self-driving cars, cognitive decision-making, communication networks, security, and signal processing. Additionally, the concept of generative networks is explored, with a particular focus on generative adversarial networks (GANs) as a powerful tool for improving the performance of existing algorithms.

In summary, this book serves as a comprehensive technological guide, spanning from fundamental theories to the classification of existing algorithms. It encompasses state-of-the-art, practical evaluation tools and methodologies, empowering readers to leverage synthetic data to enhance the performance of their applications. Whether you are a researcher, practitioner, or enthusiast, this book offers valuable insights and practical knowledge to help you navigate the world of ML and its diverse applications.

Weight: 550g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367634537