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Chris Fregly: AI Systems Performance
Engineering

AI Systems Performance Engineering

Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

The Deep Learning Revolution

A Path to Sustainable Development Goals

AI and ML Techniques in IoT-based Communication

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Generative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;&nbsp;

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Gehatnerative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.&nbsp;What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;Who This Book Is ForFrom AI enthusiasts exploring the field to software developers seeking practical insights, and business leaders looking to harness the power of AI for organizational growth and innovation&nbsp;&nbsp;

Building and Deploying Ethical AI-Powered Solutions

Practical Generative AI: From Concept to Deployment

Advances in Industrial and Systems Engineering

Advances in Human-AI Collaboration

A complete and up-to-date overview of popular and practical erasure codes in distributed storage In Distributed Storage in Practice, a team of distinguished researchers delivers a comprehensive discussion on distributed storage coding and distributed storage systems. Divided into two parts, the book first explores distributed storage coding technology based on Maximum Distance Separable (MDS) codes, including array codes, Reed-Solomon codes, locally repairable codes, and regenerating codes. It then goes on to examine the challenges presented by repairing distributed data in real-world scenarios. Distributed Storage in Practice uses two perspectives: practical optimization of distributed storage coding and emerging technologies such as blockchain. It discusses the technical foundations of blockchain and integrates blockchain into distributed storage systems. It also offers an overview of several popular blockchain-based storage systems. It also includes: <ul> <li>A thorough introduction to the current development of quantum technology, including its fundamentals, quantum memory, quantum computers, quantum security, and quantum networks</li> <li>Comprehensive explorations of data recovery methods for specific networked distributed storage scenarios</li> <li>Practical integrations of theory and practice, including classic techniques and the most recent advancements in storage coding</li> <li>A practical example of a distributed secure storage system integrated with blockchain technology</li> </ul> Perfect for researchers and undergraduate and graduate students studying computer science, Distributed Storage in Practice will also benefit blockchain professionals.

Distributed Storage in Practice

A comprehensive, up-to-date introduction to active inference and the free energy principle for an engineering-focused audience. Active inference, which uses machine learning to model brain function and behavior, emerged from decades of cross-disciplinary research in computational neuroscience, resulting in a vast literature but no unifying treatment. Filling this gap, Sanjeev Namjoshi provides comprehensive coverage of the foundational material needed to understand and navigate this fast-moving field from first principles. Using a simple, conversational style free of proofs, lemmas, and theorems, Namjoshi brings together theory and technical material in one self-contained text. The book begins with an explanation of the general statistical framework used in active inference models that describes the relationship between artificial agents and their environments. It then introduces fundamental concepts in machine learning and statistics and connects them to the active inference perspective. Featuring worked examples, simulations, and detailed walkthroughs of concepts, this user-friendly text aims to expand the readership of active inference to an engineering-focused audience.&nbsp; <ul><li>Provides a one-stop-shop for understanding the foundations and applications of active inference and the free energy principle </li><li>Makes a complex, interdisciplinary subject accessible to students and professionals beyond the neurosciences</li><li>Covers discrete and continuous state-space formulations of active inference as well as state-of-the-art extensions to base active inference methods</li><li>Features extensive appendices and supplemental resources</li></ul>

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

Fundamentals of Active Inference

Distributed and Parallel Computing

How to Make Sense of a Complex Future

The AI Paradox

Woodhead Publishing in Materials

Optical and Electrochemical Sensors for Biomedical Applications: Microstructural Analysis in Design and Performance is an up-to-date and comprehensive reference resource in this rapidly evolving field. The book’s focus is on the crucial importance that microstructural analysis plays in biomedical sensor design and performance. The chapters delve deeply into the significance of microstructure providing insights into how it influences sensor sensitivity, selectivity, and overall performance. With real-world examples, researchers will gain practical insights into the design and manufacture of sensors and their applications in diagnostics, healthcare, and medical devices. The book takes a consolidated, and practical approach making it accessible to a wide audience, which will make it an ideal choice for those who are seeking a deeper understanding of sensor design for a broad range of different biomedical applications.

Microstructural Analysis in Design and Performance

Optical and Electrochemical Sensors for Biomedical Applications

Data-Driven Modeling

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

Medical and Technical Analysis and Signal Processing Perspectives

Exploring Infant Cry

AI Systems Performance Engineering

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