Essentials of Python for Artificial Intelligence and Machine Learning by
This book introduces the essentials of Python for the emerging fields of Machine Learning (ML) and Artificial Intelligence (AI). The authors explore the use of Python's advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting, and various other applications. This includes mathematical operations with array data structures, Data Manipulation, Data Cleaning, machine learning, Data pipeline, probability density functions, interpolation, visualization, and other high-performance benefits using the core scientific packages NumPy, Pandas, SciPy, Sklearn/Scikit learn and Matplotlib. Readers will gain a deep understanding with problem-solving experience on these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. Several examples of real problems using these techniques are provided along with examples. The authors also focus on the best practices in theindustry on using Python for AI and ML. Deployment on a cloud infrastructure is described in detail (with code) to emphasize real scenarios.
Fusion and Integration of Clouds, Edges, and Devices by
This book provides an in-depth examination of recent research advances in cloud-edge-end computing, covering theory, technologies, architectures, methods, applications, and future research directions. It aims to present state-of-the-art models and optimization methods for fusing and integrating clouds, edges, and devices. Cloud-edge-end computing provides users with low-latency, high-reliability, and cost-effective services through the fusion and integration of clouds, edges, and devices. As a result, it is now widely used in various application scenarios. The book introduces the background and fundamental concepts of clouds, edges, and devices, and details the evolution, concepts, enabling technologies, architectures, and implementations of cloud-edge-end computing. It also examines different types of cloud-edge-end orchestrated systems and applications and discusses advanced performance modeling approaches, as well as the latest research on offloading and scheduling policies. It also covers resource management methods for optimizing application performance on cloud-edge-end orchestrated systems. The intended readers of this book are researchers, undergraduate and graduate students, and engineers interested in cloud computing, edge computing, and the Internet of Things. The knowledge of this book will enrich our readers to be at the forefront of cloud-edge-end computing.
Principles of Database and Solid-State Drive Co-Design by  |
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