Machine Learning Design Patterns Book Free Pdf (18 results)

Taschenbuch. Condition: Neu. Neuware - Learn how large language models like GPT and Gemini work under the hood in plain English.How Large Language Models Work translates years of expert research on Large Language Models into a readable, focused introduction to working with these amazing systems. It explains clearly how LLMs function, introduces the optimization techniques to fine-tune them, and shows how to create pipelines and processes to ensure your AI applications are efficient and error-free. In How Large Language Models Work you will learn how to: Test and evaluate LLMs Use human feedback, supervised fine-tuning, and Retrieval Augmented Generation (RAG) Reducing the risk of bad outputs, high-stakes errors, and automation bias Human-computer interaction systems Combine LLMs with traditional ML How Large Language Models Work is authored by top machine learning researchers at Booz Allen Hamilton, including researcher Stella Biderman, Director of AI/ML Research Drew Farris, and Director of Emerging AI Edward Raff. They lay out how LLM and GPT technology works in plain language that's accessible and engaging for all. About the Technology Large Language Models put the "I" in "AI." By connecting words, concepts, and patterns from billions of documents, LLMs are able to generate the human-like responses we've come to expect from tools like ChatGPT, Claude, and Deep-Seek. In this informative and entertaining book, the world's best machine learning researchers from Booz Allen Hamilton explore foundational concepts of LLMs, their opportunities and limitations, and the best practices for incorporating AI into your organizations and applications. About the Book How Large Language Models Work takes you inside an LLM, showing step-by-step how a natural language prompt becomes a clear, readable text completion. Written in plain language, you'll learn how LLMs are created, why they make errors, and how you can design reliable AI solutions. Along the way, you'll learn how LLMs "think," how to design LLM-powered applications like agents and Q&A systems, and how to navigate the ethical, legal, and security issues. What's Inside Customize LLMs for specific applications Reduce the risk of bad outputs and bias Dispel myths about LLMs Go beyond language processing About the Readers No knowledge of ML or AI systems is required. About the Author Edward Raff, Drew Farris and Stella Biderman are the Director of Emerging AI, Director of AI/ML Research, and machine learning researcher at Booz Allen Hamilton. Table of Contents 1 Big picture: What are LLMs 2 Tokenizers: How large language models see the world 3 Transformers: How inputs become outputs 4 How LLMs learn 5 How do we constrain the behavior of LLMs 6 Beyond natural language processing 7 Misconceptions, limits, and eminent abilities of LLMs 8 Designing solutions with large language models 9 Ethics of building and using LLMs Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Taschenbuch. Condition: Neu. Neuware - SummaryReal-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyMachine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand.About the BookReal-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's InsidePredicting future behaviorPerformance evaluation and optimizationAnalyzing sentiment and making recommendationsAbout the ReaderNo prior machine learning experience assumed. Readers should know Python. About the AuthorsHenrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of ContentsTHE MACHINE-LEARNING WORKFLOWWhat is machine learning Real-world dataModeling and predictionModel evaluation and optimizationBasic feature engineeringPRACTICAL APPLICATIONExample: NYC taxi dataAdvanced feature engineeringAdvanced NLP example: movie review sentimentScaling machine-learning workflowsExample: digital display advertising.

Taschenbuch. Condition: Neu. Neuware - Maximize your portfolio, analyze markets, and make data-driven investment decisions using Python and generative AI.Investing for Programmers shows you how you can turn your existing skills as a programmer into a knack for making sharper investment choices. You'll learn how to use the Python ecosystem, modern analytic methods, and cutting-edge AI tools to make better decisions and improve the odds of long-term financial success. In Investing for Programmers you'll learn how to: Build stock analysis tools and predictive models Identify market-beating investment opportunities Design and evaluate algorithmic trading strategies Use AI to automate investment research Analyze market sentiments with media data mining In Investing for Programmers you'll learn the basics of financial investment as you conduct real market analysis, connect with trading APIs to automate buy-sell, and develop a systematic approach to risk management. Don't worrythere's no dodgy financial advice or flimsy get-rich-quick schemes. Real-life examples help you build your own intuition about financial markets, and make better decisions for retirement, financial independence, and getting more from your hard-earned money. About the technology A programmer has a unique edge when it comes to investing. Using open-source Python libraries and AI tools, you can perform sophisticated analysis normally reserved for expensive financial professionals. This book guides you step-by-step through building your own stock analysis tools, forecasting models, and more so you can make smart, data-driven investment decisions. About the book Investing for Programmers shows you how to analyze investment opportunities using Python and machine learning. In this easy-to-read handbook, experienced algorithmic investor Stefan Papp shows you how to use Pandas, NumPy, and Matplotlib to dissect stock market data, uncover patterns, and build your own trading models. You'll also discover how to use AI agents and LLMs to enhance your financial research and decision-making process. What's inside Build stock analysis tools and predictive models Design algorithmic trading strategies Use AI to automate investment research Analyze market sentiment with media data mining About the reader For professional and hobbyist Python programmers with basic personal finance experience. About the author Stefan Papp combines 20 years of investment experience in stocks, cryptocurrency, and bonds with decades of work as a data engineer, architect, and software consultant. Table of Contents 1 The analytical investor 2 Investment essentials 3 Collecting data 4 Growth portfolios 5 Income portfolios 6 Building an asset monitor 7 Risk management 8 AI for financial research 9 AI agents 10 Charts and technical analysis 11 Algorithmic trading 12 Private equity: Investing in start-ups 13 The road goes ever on and on A Setting up the environment Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - From fundamental to advanced strategies, unlock useful insights for creating innovative, user-centric websites while navigating the evolving landscape of AI ethics and securityKey Features: Explore AI's role in web development, from shaping projects to architecting solutions Master advanced AI strategies to build cutting-edge applications Anticipate future trends by exploring next-gen development environments, emerging interfaces, and security considerations in AI web development Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:If you're a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience.The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you'll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you'll explore how to future-proof your web development projects using advanced AI strategies and understand AI's impact on jobs. Toward the end, you'll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape.Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain.What You Will Learn: Build AI-powered web projects with optimized models Personalize UX dynamically with AI, NLP, chatbots, and recommendations Explore AI coding assistants and other tools for advanced web development Craft data-driven, personalized experiences using pattern recognition Architect effective AI solutions while exploring the future of web development Build secure and ethical AI applications following TRiSM best practices Explore cutting-edge AI and web development trendsWho this book is for:This book is for web developers with experience in programming languages and an interest in keeping up with the latest trends in AI-powered web development. Full-stack, front-end, and back-end developers, UI/UX designers, software engineers, and web development enthusiasts will also find valuable information and practical guidelines for developing smarter websites with AI. To get the most out of this book, it is recommended that you have basic knowledge of programming languages such as HTML, CSS, and JavaScript, as well as a familiarity with machine learning concepts.Table of Contents AI's Role in Shaping Web Development Mastering the Essentials - AI Fundamentals Challenges and Opportunities - Integrating AI into Web Projects Navigating the Landscape - Popular AI and ML Frameworks and Tools Blueprints of the Future - Architecting Effective AI Solutions Design Intelligence - Creating User-Centric Experiences with AI Recognizing Patterns - Personalizing User Journeys with AI Coding Assistants - Your Secret Weapon in Modern Development Smarter User Interactions - Elevating User Engagement with Advanced AI Smart Testing Strategies - Fortifying Web Applications with AI Insights Augmented Workforce - AI's Impact on Web Development Jobs(N.B. Please use the Read Sample option to see further chapters).

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Turn challenges into opportunities by learning advanced techniques for text generation, summarization, and question answering using LangChain and Google Cloud toolsKey Features: Solve real-world business problems with hands-on examples of GenAI applications on Google Cloud Learn repeatable design patterns for Gen AI on Google Cloud with a focus on architecture and AI ethics Build and implement GenAI agents and workflows, such as RAG and NL2SQL, using LangChain and Vertex AI Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:The rapid transformation and enterprise adoption of GenAI has created an urgent demand for developers to quickly build and deploy AI applications that deliver real value. Written by three distinguished Google AI engineers and LangChain contributors who have shaped Google Cloud's integration with LangChain and implemented AI solutions for Fortune 500 companies, this book bridges the gap between concept and implementation, exploring LangChain and Google Cloud's enterprise-ready tools for scalable AI solutions.You'll start by exploring the fundamentals of large language models (LLMs) and how LangChain simplifies the development of AI workflows by connecting LLMs with external data and services. This book guides you through using essential tools like the Gemini and PaLM 2 APIs, Vertex AI, and Vertex AI Search to create sophisticated, production-ready GenAI applications. You'll also overcome the context limitations of LLMs by mastering advanced techniques like Retrieval-Augmented Generation (RAG) and external memory layers.Through practical patterns and real-world examples, you'll gain everything you need to harness Google Cloud's AI ecosystem, reducing the time to market while ensuring enterprise scalability. You'll have the expertise to build robust GenAI applications that can be tailored to solve real-world business challenges.What You Will Learn: Build enterprise-ready applications with LangChain and Google Cloud Navigate and select the right Google Cloud generative AI tools Apply modern design patterns for generative AI applications Plan and execute proof-of-concepts for enterprise AI solutions Gain hands-on experience with LangChain's and Google Cloud's AI products Implement advanced techniques for text generation and summarization Leverage Vertex AI Search and other tools for scalable AI solutionsWho this book is for:If you're an application developer or ML engineer eager to dive into GenAI, this book is for you. Whether you're new to LangChain or Google Cloud, you'll learn how to use these tools to build scalable AI solutions. This book is ideal for developers familiar with Python and machine learning basics looking to apply their skills in GenAI. Professionals who want to explore Google Cloud's powerful suite of enterprise-grade GenAI products and their implementation will also find this book useful.Table of Contents: Using LangChain with Google Cloud Foundational Models on Google Cloud Grounding Responses on Google Cloud Vector Search on Google Cloud Advanced Techniques for Parsing and Ingesting Documents Multimodality Working with Long Context Building Chatbots Tools and Function Calling Agents in Generative AI Agentic Workflows Evaluating GenAI Applications GenAI System Design Appendix 1 - Overview of Generative AI Appendix 2 - Google Cloud Foundations.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Explore Detectron2 using cutting-edge models and learn all about implementing future computer vision applications in custom domainsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features: Learn how to tackle common computer vision tasks in modern businesses with Detectron2 Leverage Detectron2 performance tuning techniques to control the model's finest details Deploy Detectron2 models into production and develop Detectron2 models for mobile devicesBook Description:Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It's used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment.The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You'll get to grips with the theories and visualizations of Detectron2's architecture and learn how each module in Detectron2 works. As you advance, you'll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you'll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices.By the end of this deep learning book, you'll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2.What You Will Learn: Build computer vision applications using existing models in Detectron2 Grasp the concepts underlying Detectron2's architecture and components Develop real-life projects for object detection and object segmentation using Detectron2 Improve model accuracy using Detectron2's performance-tuning techniques Deploy Detectron2 models into server environments with ease Develop and deploy Detectron2 models into browser and mobile environmentsWho this book is for:If you are a deep learning application developer, researcher, or software developer with some prior knowledge about deep learning, this book is for you to get started and develop deep learning models for computer vision applications. Even if you are an expert in computer vision and curious about the features of Detectron2, or you would like to learn some cutting-edge deep learning design patterns, you will find this book helpful. Some HTML, Android, and C++ programming skills are advantageous if you want to deploy computer vision applications using these platforms.Table of Contents An Introduction to Detectron2 and Computer Vision Tasks Developing Computer Vision Applications Using Existing Detectron2 Models Data Preparation for Object Detection Applications The Architecture of the Object Detection Model in Detectron2 Training Custom Object Detection Models Inspecting Training Results and Fine-Tuning Detectron2's Solver Fine-Tuning Object Detection Models Image Data Augmentation Techniques Applying Train-Time and Test-Time Image Augmentations Training Instance Segmentation Models Fine-Tuning Instance Segmentation Models Deploying Detectron2 Models into Server Environments Deploying Detectron2 models into Browsers and Mobile Environments.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build robust cross-platform apps with practical recipes covering UI best practices and performance optimization to authentication, offline data synchronization, and AI integrationKey Features: Follow step-by-step recipes with best practices for a performant UI and structured business logic Perform essential modern tasks like integration with Web API, Google OAuth, SignalR, and AI Check out additional sections for deep understanding, common pitfalls, and GitHub examples Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Think about how much time you usually spend building an app in a technology you're still mastering-grasping new concepts, navigating roadblocks, and even rewriting entire modules as you learn. This book saves you that time, helping you create a modern .NET MAUI application like a pro.The chapters address a wide range of tasks and concepts essential for real-world apps, including UI best practices and advanced tips, MVVM, dependency injection, performance, and memory profiling. Since real-world applications often go beyond frontend development, this book also explores integration with backend services for authentication, data processing, synchronization, and real-time updates. Additionally, you'll learn to implement multiple AI integration strategies, all without any prior machine learning experience.Mastery comes with practice, so the book is organized with step-by-step recipes, each tackling a specific task. Each recipe includes detailed explanations to help you apply what you're learning to your own unique projects.By the end of this book, you'll have developed the skills to build high-performance, interactive cross-platform applications with .NET MAUI, saving valuable time on your future projects.What You Will Learn: Discover effective techniques for creating robust, adaptive layouts Leverage MVVM, DI, cached repository, and unit of work patterns Integrate authentication with a self-hosted service and Google OAuth Incorporate session management and role-based data access Tackle real-time updates, chunked file uploads, and offline data mode Explore AI integration strategies, from local device to cloud models Master techniques to fortify your app with platform-specific APIs Identify and eliminate performance and memory issuesWho this book is for:This book is for intermediate developers familiar with .NET MAUI basics, and is perfect for those looking to deepen their understanding and refine their skills for creating cross-platform applications and delivering top-quality applications. The book offers advanced techniques and practical examples for handling real-world development challenges effectively.Table of Contents Crafting the Page Layout Mastering the MVVM Design Pattern Advanced XAML and UI Techniques Connecting to a Database and Implementing CRUD Operations Authentication and Authorization Real-Life Scenarios: AI, SignalR, and More Understanding Platform-Specific APIs and Custom Handlers Optimizing Performance.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF Elektronisches Buch.Key Features:Explore the architecture and different use cases to build and manage geospatial data lakes in AWSDiscover how to leverage AWS purpose-built databases to store and analyze geospatial dataLearn how to recognize which anti-patterns to avoid when managing geospatial data in the cloudBook Description:Managing geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.You'll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you'll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you'll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.By the end of this book, you'll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.What You Will Learn:Discover how to optimize the cloud to store your geospatial dataExplore management strategies for your data repository using AWS Single Sign-On and IAMCreate effective SQL queries against your geospatial data using AthenaValidate postal addresses using Amazon Location servicesProcess structured and unstructured geospatial data efficiently using RUse Amazon SageMaker to enable machine learning features in your applicationExplore the free and subscription satellite imagery data available for use in your GISWho this book is for:If you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWSPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey FeaturesGo in-depth into the ML lifecycle, from ideation and data management to deployment and scalingApply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutionsUnderstand the generative AI lifecycle, its core technologies, and implementation risksBook DescriptionDavid Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills.You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You'll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI.By the end of this book , you'll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You'll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.What you will learnApply ML methodologies to solve business problems across industriesDesign a practical enterprise ML platform architectureGain an understanding of AI risk management frameworks and techniquesBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using artificial intelligence services and custom modelsDive into generative AI with use cases, architecture patterns, and RAGWho this book is forThis book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.Table of ContentsNavigating the ML Lifecycle with ML Solutions ArchitectureExploring ML Business Use CasesExploring ML AlgorithmsData Management for MLExploring Open-Source ML LibrariesKubernetes Container Orchestration Infrastructure ManagementOpen-Source ML PlatformsBuilding a Data Science Environment using AWS ML ServicesDesigning an Enterprise ML Architecture with AWS ML ServicesAdvanced ML EngineeringBuilding ML Solutions with AWS AI ServicesAI Risk ManagementBias, Explainability, Privacy, and Adversarial Attacks(N.B. Please use the Read Sample option to see further chapters).

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build optimized games & elevate your skills with popular software design patterns in Unity 2023 and C#!Purchase of the print or Kindle book includes a free Elektronisches Buch in PDF formatKey Features: Craft engaging Unity 2023 games while mastering design patterns like Singleton, Object Pool, and more Write clean, reusable C# code using creational, behavioral, and structural patterns, tailored for the game development environment Go beyond basic design pattern usage and learn to customize and extend them for your unique game design needsBook Description:Struggling to write maintainable and clean code for your Unity games Look no further! Learning Design Patterns with Unity empowers you to harness the fullest potential of popular design patterns while building exciting Unity projects. Through hands-on game development, you'll master creational patterns like Prototype to efficiently spawn enemies and delve into behavioral patterns like Observer to create reactive game mechanics. As you progress, you'll also identify the negative impacts of bad architectural decisions and understand how to overcome them with simple but effective practices.By the end of this Unity 2023 book, the way you develop Unity games will change. You'll emerge not just as a more skilled Unity developer, but as a well-rounded software engineer equipped with industry-leading design patterns.What You Will Learn: Implement a persistent game manager using the Singleton pattern Spawn projectiles efficiently with Object Pooling for optimized performance Build a flexible crafting system using the Factory Method pattern Design an undo/redo system for player movement with the Command pattern Implement a state machine to control a two-person battle system Modify existing character objects with special abilities using the Decorator patternWho this book is for:This book is your perfect companion if you're a Unity game developer looking to level up your C# skills and embrace industry standards for building robust games. Knowledge of Unity and basic C# programming is recommended.Table of Contents Priming the system Managing access with the Singleton pattern Spawning enemies with the Prototype pattern Creating items with the Factory Method pattern Building a crafting system with the Abstract Factory pattern Assembling support characters with the Builder pattern Managing Performance and Memory with Object Pooling Binding actions with the Command pattern Decoupling systems with the Observer pattern Controlling characters with the State pattern Adding Features with the Visitor Pattern Swapping Algorithms with the Strategy pattern Making Monsters with the Type Object Pattern Taking Data Snapshots with the Memento Pattern Dynamic Upgrades with the Decorator Pattern Converting incompatible classes with the Adapter pattern Simplifying subsystems with the Facade pattern Generating terrains with the Flyweight pattern Global access with the Service Locator pattern The Road Ahead.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect's perspective to developing end-to-end solutionsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Translate and implement conceptual architectures with the right Azure servicesInject artificial intelligence into data solutions for advanced analyticsLeverage cloud computing and frameworks to drive data science workloadsBook Description:With data's growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution.You'll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you'll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you'll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you'll discover how to add data governance, compliance, and security to solutions.By the end of this book, you'll have gained the expertise needed to become a well-rounded Azure Data & AI architect.What You Will Learn:Design scalable and cost-effective cloud data platforms on Microsoft AzureExplore architectural design patterns with various use casesDetermine the right data stores and data warehouse solutionsDiscover best practices for data orchestration and transformationHelp end users to visualize data using interactive dashboardingLeverage OpenAI and custom ML models for advanced analyticsManage security, compliance, and governance for the data estateWho this book is for:This book is for anyone looking to elevate their skill set to the level of an architect. Data engineers, data scientists, business intelligence developers, and database administrators who want to learn how to design end-to-end data solutions and get a bird's-eye view of the entire data platform will find this book useful. Although not required, basic knowledge of databases and data engineering workloads is recommended.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and KafkaKey Features:Develop modern data skills used in emerging technologiesLearn pragmatic design methodologies such as Data Mesh and data lakehousesGain a deeper understanding of data governancePurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You'll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake.Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You'll gain an understanding of not Elektronisches Buch and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you'll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you'll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you'll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you'll get hands-on experience with Apache Spark, one of the key data technologies in today's market.By the end of this book, you'll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What You Will Learn:Understand data patterns including delta architectureDiscover how to increase performance with Spark internalsFind out how to design critical data diagramsExplore MLOps with tools such as AutoML and MLflowGet to grips with building data products in a data meshDiscover data governance and build confidence in your dataIntroduce data visualizations and dashboards into your data practiceWho this book is for:This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they're not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Elevate your natural language processing skills with Gensim and become proficient in handling a wide range of NLP tasks and projectsKey FeaturesAdvance your NLP skills with this comprehensive guide covering detailed explanations and code practicesBuild real-world topical modeling pipelines and fine-tune hyperparameters to deliver optimal resultsAdhere to the real-world industrial applications of topic modeling in medical, legal, and other fieldsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook DescriptionNavigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios.You'll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy.Next, you'll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you'll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications.By the end of this book, you'll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.What you will learnConvert text into numerical values such as bag-of-word, TF-IDF, and word embeddingUse various NLP techniques with Gensim, including Word2Vec, Doc2Vec, LSA, FastText, LDA, and Ensemble LDABuild topical modeling pipelines and visualize the results of topic modelsImplement text summarization for legal, clinical, or other documentsApply core NLP techniques in healthcare, finance, and e-commerceCreate efficient chatbots by harnessing Gensim's NLP capabilitiesWho this book is forThis book is for data scientists and professionals who want to become proficient in topic modeling with Gensim. NLP practitioners can use this book as a code reference, while students or those considering a career transition will find this a valuable resource for advancing in the field of NLP. This book contains real-world applications for biomedical, healthcare, legal, and operations, making it a helpful guide for project managers designing their own topic modeling applications.Table of ContentsIntroduction to NLPWord EmbeddingText Wrangling and PreprocessingLatent Semantic Analysis with scikit-learnCosine SimilarityLatent Semantic Indexing with GensimUsing Word2VecDoc2Vec with GensimUnderstanding Discrete DistributionsLatent Dirichlet AllocationLDA ModelingLDA VisualizationThe Ensemble LDA for Model StabilityLDA and BERTopicReal-World Use Cases.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platformsKey Features: Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Revolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines.You'll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you'll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications.By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What You Will Learn: Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelinesWho this book is for:If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.Table of Contents Overview of the Business Problem Statement A Data Engineer's Journey - Background Challenges A Data Engineer's Journey - IT's Vision and Mission Architecture Principles Architecture Framework - Conceptual Architecture Best Practices Architecture Framework - Logical Architecture Best Practices Architecture Framework - Physical Architecture Best Practices Software Engineering Best Practice Considerations Key Considerations for Agile SDLC Best Practices Key Considerations for Quality Testing Best Practices Key Considerations for IT Operational Service Best Practices(N.B. Please use the Read Sample option to see further chapters).

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectivelyKey Features: Understand key concepts, from fundamentals through to complex topics, via a methodical approach Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Most companies today are incorporating AI/ML into their businesses. Building and running apps utilizing AI/ML effectively is tough. This book, authored by a principal architect with about two decades of industry experience, who has led cross-functional teams to design, plan, implement, and govern enterprise cloud strategies, shows you exactly how to design and run AI/ML workloads successfully using years of experience from some of the world's leading tech companies.You'll get a clear understanding of essential fundamental AI/ML concepts, before moving on to complex topics with the help of examples and hands-on activities. This will help you explore advanced, cutting-edge AI/ML applications that address real-world use cases in today's market. You'll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You'll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process.By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.What You Will Learn: Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark Source, understand, and prepare data for ML workloads Build, train, and deploy ML models on Google Cloud Create an effective MLOps strategy and implement MLOps workloads on Google Cloud Discover common challenges in typical AI/ML projects and get solutions from experts Explore vector databases and their importance in Generative AI applications Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflowsWho this book is for:This book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.Table of Contents AI/ML Concepts, Real-World Applications, and Challenges Understanding the ML Model Development Lifecycle AI/ML Tooling and the Google Cloud AI/ML Landscape Utilizing Google Cloud's High-Level AI Services Building Custom ML Models on Google Cloud Diving Deeper-Preparing and Processing Data for AI/ML Workloads on Google Cloud Feature Engineering and Dimensionality Reduction Hyperparameters and Optimization Neural Networks and Deep Learning Deploying, Monitoring, and Scaling in Production Machine Learning Engineering and MLOps with GCP(N.B. Please use the Read Sample option to see further chapters).

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Take your C++ skills to the next level with expert insights on advanced techniques, design patterns, and high-performance programmingPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Master templates, metaprogramming, and advanced functional programming techniques to elevate your C++ skillsDesign scalable and efficient C++ applications with the latest features of C++17 and C++20Explore real-world examples and essential design patterns to optimize your codeBook Description:Are you an experienced C++ developer eager to take your skills to the next level This updated edition of Expert C++ is tailored to propel you toward your goals.This book takes you on a journey of building C++ applications while exploring advanced techniques beyond object-oriented programming. Along the way, you'll get to grips with designing templates, including template metaprogramming, and delve into memory management and smart pointers. Once you have a solid grasp of these foundational concepts, you'll advance to more advanced topics such as data structures with STL containers and explore advanced data structures with C++. Additionally, the book covers essential aspects like functional programming, concurrency, and multithreading, and designing concurrent data structures. It also offers insights into designing world-ready applications, incorporating design patterns, and addressing networking and security concerns. Finally, it adds to your knowledge of debugging and testing and large-scale application design.With Expert C++ as your guide, you'll be empowered to push the boundaries of your C++ expertise and unlock new possibilities in software development.What You Will Learn:Go beyond the basics to explore advanced C++ programming techniquesDevelop proficiency in advanced data structures and algorithm design with C++17 and C++20Implement best practices and design patterns to build scalable C++ applicationsMaster C++ for machine learning, data science, and data analysis framework designDesign world-ready applications, incorporating networking and security considerationsStrengthen your understanding of C++ concurrency, multithreading, and optimizing performance with concurrent data structuresWho this book is for:This book will empower experienced C++ developers to achieve advanced proficiency, enabling them to build professional-grade applications with the latest features of C++17 and C++20. If you're an aspiring software engineer or computer science student, you'll able to master advanced C++ programming techniques through real-world applications that will prepare you for complex projects and real-world challenges.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production-ideal for Python developers building GenAI applicationsKey Features: Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:This second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines.You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs-complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy.Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What You Will Learn: Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practicesWho this book is for:This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it's especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.Table of Contents The Rise of Generative AI: From Language Models to Agents First Steps with LangChain Building Workflows with LangGraph Building Intelligent RAG Systems with LangChain Building Intelligent Agents Advanced Applications and Multi-Agent Systems Software Development and Data Analysis Agents Evaluation and Testing Observability and Production Deployment The Future of LLM Applications.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainabilityPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Learn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake explainable predictions for privacy and fairness-enabled ML trainingBook Description:AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent.You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics.By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.What You Will Learn:Understand the threats and risks involved in ML modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate privacy and fairness in ML models from design to deploymentWho this book is for:This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.