Dilip K. Prasad – författare

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. 

The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

 

 

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

This book explores cloud computing, starting with fundamental concepts, history, characteristics, service and deployment models. It covers cloud security and privacy, including threats, mechanisms, and best practices. The book then examines cloud migration strategies and cost management, including TCO and ROI. It explains cloud architecture, design principles, application development, and advanced technologies such as edge computing, AI/ML services, and multi-cloud strategies. Cloud-based data management and analytics are addressed, along with cloud storage solutions and analytics platforms. Sustainability in cloud computing is also covered, focusing on green practices and environmental considerations. The book concludes with hands-on projects using Azure, providing practical experience in creating virtual machines, networking, deploying web applications, and performing other cloud-related tasks.