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Gesamtkatalog

Catalogue complet

Muhammad Fasih Ullah: Advanced Snowflake

Advanced Snowflake

Processing Data, Developing Applications, and Deploying ML Models at Scale

Dissecting the Tools of the Underground Economy

Reversing the Dark Web

Build the microchips of the future with this revolutionary new information transfer technology Advancements in high-performance computing have continually demanded for progresses in disruptive technological research and innovations. Moore's Law has pushed the Very Large Scale Integration (VLSI) technology to pack MOS devices inside a chip at an exponential rate, thereby surpassing now eight billion transistors per cm2. This has concomitantly fueled the growth of multilayered on-chip interconnects comprising metallic and low dielectric materials. VLSI Interconnect Technology with Artificial Plasmonics introduces a new method for improving chip performance by harnessing the power of information transfer among chips at terahertz frequency. This revolutionary new electromagnetic wave engineering, called a spoof surface plasmon polariton, adapts the principles of VLSI and terahertz interconnect technology along with the artificial plasmonics to transfer huge quantities of data at vastly improved speeds. It constitutes a potentially decisive contribution to the pursuit of faster and more capacious VLSI chips. In VLSI Interconnect Technology with Artificial Plasmonics, readers will also find: <ul> <li>A cutting-edge new approach supported by pioneering research</li> <li>Detailed discussion of essential components related to the development of THz interconnect technology, including theory, modeling, simulation, and validation</li> <li>Roadmap to future technological development in the branch of artificial plasmonics</li> </ul> VLSI Interconnect Technology with Artificial Plasmonics is ideal for engineers, researchers, and scientists working in electronics, electromagnetics, and optics.

Bridging the Gap between Electronics and Optics

VLSI Interconnect Technology with Artificial Plasmonics

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: <ul> <li>A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes</li> <li>Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML</li> <li>Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis</li> </ul> Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Tiny Machine Learning: Design Principles and Applications

Praxisleitfaden für das IT- und Software Asset Management

1x1 des Lizenzmanagements

Quantum Computing, Machine Learning, and Bio-inspired Optimization

Advanced Techniques for Optimal Sizing of Analog Integrated Circuits

Manage the complexity of AI in any organization with this actionable guide to operationalizing AI governance, navigating regulatory challenges and future-proofing systems.

Building a Program for Strategy and Oversight

Practical AI Governance

Tiny Machine Learning Techniques for Constrained Devices

A no-fluff guide to implementing best security practices for microservices in the cloud written by a team of experienced security engineers at Snapchat. This book takes you through every step of secure software development and deployment for microservices – the discrete functions within a larger application. Readers begin by exploring core security tenets, then dive into developing microservices and securing infrastructure. Along the way, they’ll gain the skills to think like a security engineer at scale and effectively plan secure designs. Each chapter provides step-by-step explanations of vital concepts, practical examples, mistakes to look out for, and quizzes to test the reader’s knowledge. Whether you’re a one-person security team or a larger group, this book equips you with the skills to build a full-fledged microservices security discipline.

Securing Cloud Native Apps

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Hacking and Securing Serverless Cloud Applications on AWS, Azure, and GCP

Learning Serverless Security

Advanced Snowflake

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