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Konieczny B: Data Engineering Design
Patterns

Data Engineering Design Patterns

Recipes for Solving the Most Common Data Engineering Problems

Tiny Machine Learning Techniques for Constrained Devices

Malware detection is exceedingly challenging, requiring deeper expertise in two domains: machine learning (ML) and malware analysis.This comprehensive collection guides ML practitioners and researchers into the state-of-the-art and underlying insights needed to work in and research problems at the intersection of machine learning and cyber security, particularly around the problem of malware targeting the Windows platform.This useful volume's contributions come from a range of experts, representing large companies, major universities, and government labs, all working at the frontiers of malware detection and remediation research.

Machine Learning For Malware Detection

Techniques and Applications

Deep Learning for Intrusion Detection

An Introduction to String Algorithms

The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Designing Data-Intensive Applications

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

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

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.

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

An essential companion to The Rust Programming Language, 2nd Edition, offering targeted exercises to enhance beginners' skills in reading and writing Rust code. The Rust Programming Language Workbook provides short coding exercises inspired by the rustlings project and organized by topic. The workbook starts with concepts common to many programming languages today like variables, data types, and functions then moves on to important Rust concepts like move semantics, structs, and enums. Short introductions of the concepts are followed by exercises that are intentionally broken. Readers need to change the code or add code to fix each exercise. The workbook also provides hints and example solutions to help beginners get unstuck.

The Rust Programming Language Workbook

Sustainable Supply Chain Management: Computational Approaches and Modelling provides a comprehensive understanding of how computational approaches can be leveraged to address the challenges associated with sustainable supply chain management. It offers insights into emerging and advanced modelling techniques to optimize supply chain operations while considering environmental, social, and economic factors. This book will be a valuable resource for researchers, practitioners, and policymakers interested in understanding and implementing sustainable supply chain management practices through computational approaches and modelling. This book helps researchers understand the opportunities, challenges, and solutions by developing innovative computational tools and techniques to integrate sustainability considerations into supply chain decision-making processes.

Computational Approaches and Modelling

Sustainable Supply Chain Management

A Path to Sustainable Development Goals

AI and ML Techniques in IoT-based Communication

Written by a data scientist with decades of practical experience on some of the most challenging datasets, this book caters to both novices and professionals working in the fields of data science and machine learning. It enables novices to enter these fields with confidence and ease. There are no formal prerequisites. An introduction to Python is included. The book then proceeds to cover some of the language's most advanced features. The mathematical foundations that follow are meticulously explained. The discussion then proceeds to a practical consideration of data science and machine learning. The material is delivered through case studies developed on real datasets. The reader is invited to follow these case studies, and so develop experience in working with real data. Numerous solved exercises make this work suitable for self-study. The contents of this book have been taught by the author to students of different age groups and levels of experience at Imperial College London and at the Thalesians Intensive Summer School in Artificial Intelligence over the years.

Introduction To Python For Quantitative Finance, An: From Scratch To Productivity

Data Engineering Design Patterns

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