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Get the eBook free when you register your print book at Manning."e;A practical guide for anyone who wants to move beyond demos and create agentic systems with confidence. Elliot Kim, Coupang Inc. This book is a fascinating step-by-step journey through design, development, and deployment of a system of autonomous AI agents. Authors Jungjun Hur and Younghee Song guide you concept-by-concept as you build a sophisticated research agent in Python, designed to solve complex, multi-step tasks from the rigorous GAIA (General AI Assistants) benchmark. By seeing the construction of the system end to end, youll understand how the underlying mechanics unobstructed by any existing blackbox libraries and frameworks. This book includes ten meaty chapters and a handy appendix (about how to get an OpenAI API key). The technical explanations throughout the book are made crystal clear by the use of many helpful diagrams. As one reviewer remarked, even for someone building their first agent, nothing feels like magic or a mystery after the first few chapters! Early on, youll establish the foundational blueprint for your agent by distinguishing rigid developer-defined workflows from true, LLM-directed agent loops. Youll learn how to manage stateless APIs, enforce structured outputs using Pydantic, and implement dynamic tool-calling. Crucially, the book centers on Context Engineeringthe discipline of systematically structuring information to prevent context rot and the Lost in the Middle effect. One technical reviewer particularly appreciated that core concepts like agent loops, context engineering, and evaluation are introduced with clear motivation and practical examples rather than as isolated abstractions. After you build the basic agent, youll progressively expand its capabilities by integrating custom web search, local file system exploration, and vector-based RAG to navigate complex data. Youll also enable the agent to recall past failures and adapt state persistence through hierarchical context optimization and longterm memory. Along the way, youll see how to use the Model Context Protocol (MCP) to seamlessly connect your agent to external tools and servers. Later chapters push into advanced territory. To prevent the agent from hallucinating or looping during complex tasks, youll implement explicit planning and procedural reflection. Then, youll explore the Code-Act paradigm, empowering the agent with sandboxed cloud environments to securely write scripts, run CLI commands, and compose tools dynamically. Finally, youll up-scale your project from a single agent into a collaborative, multi-agent architecture using the A2A protocol, where specialized tasks are routed to isolated sub-agents to maximize token efficiency. What's inside End-to-end construction of a framework-free, autonomous AI agent in Python Advanced practical integrations including MCP, vector-based RAG, long-term memory, and sandboxed code execution Multi-agent orchestration and automated LLM-as-a-judge evaluation pipelines About the reader For Python developers with some knowledge of machine learning who want to build and deeply understand agentic systems without relying on abstract libraries. About the author Jungjun Hur is an independent AI and data engineer who specializes in building agentic systems for e-commerce. He is the author of Practical AI Application Development Using LLMs. Younghee Song is an AI consultant at PricewaterhouseCoopers (PwC). Both authors live and work in South Korea.

A practical introduction to data engineering on the powerful Snowflake cloud data platform.Data engineers create the pipelines that ingest raw data, transform it, and funnel it to the analysts and professionals who need it. The Snowflake cloud data platform provides a suite of productivity-focused tools and features that simplify building and maintaining data pipelines. In Snowflake Data Engineering, Snowflake Data Superhero Maja Ferle shows you how to get started. In Snowflake Data Engineering you will learn how to: • Ingest data into Snowflake from both cloud and local file systems • Transform data using functions, stored procedures, and SQL • Orchestrate data pipelines with streams and tasks, and monitor their execution • Use Snowpark to run Python code in your pipelines • Deploy Snowflake objects and code using continuous integration principles • Optimize performance and costs when ingesting data into Snowflake Snowflake Data Engineering reveals how Snowflake makes it easy to work with unstructured data, set up continuous ingestion with Snowpipe, and keep your data safe and secure with best-in-class data governance features. Along the way, you’ll practice the most important data engineering tasks as you work through relevant hands-on examples. Throughout, author Maja Ferle shares design tips drawn from her years of experience to ensure your pipeline follows the best practices of software engineering, security, and data governance. Foreword by Joe Reis. About the technology Pipelines that ingest and transform raw data are the lifeblood of business analytics, and data engineers rely on Snowflake to help them deliver those pipelines efficiently. Snowflake is a full-service cloud-based platform that handles everything from near-infinite storage, fast elastic compute services, inbuilt AI/ML capabilities like vector search, text-to-SQL, code generation, and more. This book gives you what you need to create effective data pipelines on the Snowflake platform. About the book Snowflake Data Engineering guides you skill-by-skill through accomplishing on-the-job data engineering tasks using Snowflake. You’ll start by building your first simple pipeline and then expand it by adding increasingly powerful features, including data governance and security, adding CI/CD into your pipelines, and even augmenting data with generative AI. You’ll be amazed how far you can go in just a few short chapters! What''s inside • Ingest data from the cloud, APIs, or Snowflake Marketplace • Orchestrate data pipelines with streams and tasks • Optimize performance and cost About the reader For software developers and data analysts. Readers should know the basics of SQL and the Cloud. About the author Maja Ferle is a Snowflake Subject Matter Expert and a Snowflake Data Superhero who holds the SnowPro Advanced Data Engineer and the SnowPro Advanced Data Analyst certifications. Table of Contents Part 1 1 Data engineering with Snowflake 2 Creating your first data pipeline Part 2 3 Best practices for data staging 4 Transforming data 5 Continuous data ingestion 6 Executing code natively with Snowpark 7 Augmenting data with outputs from large language models 8 Optimizing query performance 9 Controlling costs 10 Data governance and access control Part 3 11 Designing data pipelines 12 Ingesting data incrementally 13 Orchestrating data pipelines 14 Testing for data integrity and completeness 15 Data pipeline continuous integration

Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up!In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: • Plan and code all the parts of an LLM • Prepare a dataset suitable for LLM training • Fine-tune LLMs for text classification and with your own data • Use human feedback to ensure your LLM follows instructions • Load pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. About the book Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself! What''s inside • Plan and code an LLM comparable to GPT-2 • Load pretrained weights • Construct a complete training pipeline • Fine-tune your LLM for text classification • Develop LLMs that follow human instructions About the reader Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. About the author Sebastian Raschka, PhD, is an LLM Research Engineer with over a decade of experience in artificial intelligence. His work spans industry and academia, including implementing LLM solutions as a senior engineer at Lightning AI and teaching as a statistics professor at the University of Wisconsin–Madison. Sebastian collaborates with Fortune 500 companies on AI solutions and serves on the Open Source Board at University of Wisconsin–Madison. He specializes in LLMs and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations. He is the author of the bestselling books Machine Learning with PyTorch and Scikit-Learn, and Machine Learning Q and AI. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA

Get the eBook free when you register your print book at Manning.This book introduces deep learning from scratch with examples that use the R language and the Keras library. Each chapter offers practical code examples that build your understanding of deep learning layer by layer. Youll appreciate the intuitive explanations, crisp illustrations, and clear examples. In this expanded third edition youll find fresh chapters on the transformers architecture, building your own GPT-like large language model, and image generation with diffusion models. Plus, even DL veterans will benefit from the insightful explanations on the nature of deep learning.For R programmers, the R interface to the Keras deep learning library is a powerful head start on building deep learning models without switching to Python. It provides a simple, consistent API that makes deep learning accessible and simplifies the process of building neural networks, even if you have no prior experience in advanced machine learning.In Deep Learning with R, Third Edition you will learn: Deep learning from first principles The latest features of Keras Image classification and image segmentation Time series forecasting Text classification and machine translation Text and image generationbuild your own LLMs and diffusion models! Scaling and tuning modelsAbout the technologyDeep Learning with R, Third Edition is a practical, concept-driven introduction to modern deep learning for R users. With a focus on clarity, intuition, and hands-on experimentation, it guides you from the foundations of deep learning to advanced architectures such as transformers and LLMs. This book treats R as a fully capable environment for modern deep learning, showing how contemporary models and workflows can be developed end to end without compromise.About the bookDeep Learning with R, Third Edition gets you up to speed with the current state of deep learning practice. Using Keras 3 with R, youll build and train neural networks from scratch, work with transformers, fine-tune pretrained models and explore large language models and diffusion-based image generation. By following carefully constructed examples that build insight step-by-step, youll develop a deep understanding of why these models worknot just how to use them.What's inside Hands-on, code-first learning in R A clear progression from deep learning fundamentals to generative AI Examples that emphasize intuition and understandingAbout the readerFor readers with intermediate R skills. No prior experience with deep learning is required.About the authorFranois Chollet is the creator of Keras and author of Deep Learning with Python. Tomasz Kalinowski is a software engineer at Posit Software, PBC and maintainer of the Keras and TensorFlow R packages.Table of Contents1 What is deep learning?2 The mathematical building blocks of neural networks3 Introduction to TensorFlow, PyTorch, JAX, and Keras4 Classification and regression5 Fundamentals of machine learning6 The universal workflow of machine learning7 A deep dive into Keras8 Image classification9 Convnet architecture patterns10 Interpreting what convnets learn11 Image segmentation12 Object detection13 Timeseries forecasting14 Text classification15 Language models and the Transformer16 Text generation17 Image generation18 Best practices for the real world19 The future of AI20 Conclusions

SummaryA Web API is a platform with a web-style interface developers can use to implement functionality. Well-designed APIs feel like a natural extension of the application, rather than just a new interface into the backend database. Designing Web APIs based on use cases allows an organization to develop irresistible APIs, which developers can consume easily and which support the business values of that organization.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyIt takes a village to deliver an irresistible web API. Business stakeholders look for an API that works side-by-side with the main product to enhance the experience for customers. Project managers require easy integration with other products or ways for customers to interact with your system. And, developers need APIs to consistently interoperate with external systems. The trick is getting the whole village together. This book shows you how.About the BookIrresistible APIspresents a process to create APIs that succeed for all members of the team. In it, you''ll learn how to capture an application''s core business value and extend it with an API that will delight the developers who use it. Thinking about APIs from the business point of view, while also considering the end-user experience, encourages you to explore both sides of the design process and learn some successful biz-to-dev communication patterns. Along the way, you''ll start to view your APIs as part of your product''s core value instead of just an add-on.What''s InsideDesign-driven developmentDeveloping meaningful use casesAPI guiding principlesHow to recognize successful APIsAbout the ReaderWritten for all members of an API design team, regardless of technical level.About the AuthorKirsten Hunter is an API evangelist who helps developers and business stakeholders understand, design, and deliver amazing APIs.Table of Contents

UNDERSTANDING WEB APIs

What makes an API irresistible?Working with web APIsAPI FirstWeb services explained

DESIGNING WEB APIs

Guiding principles for API designDefining the value for your APICreating your schema modelDesign-driven developmentEmpowering your developers

Proven techniques and principles for modernizing legacy systems into new architectures that deliver serious competitive advantage.For a business to thrive, it needs a modern software architecture that is aligned with its corporate architecture. This book presents concrete practices that sync software, product, strategy, team dynamics, and work practices. You’ll evolve your technical and social architecture together, reducing needless dependencies and achieving faster flow of innovation across your organization. In Architecture Modernization: Socio-technical alignment of software, strategy, and structure you’ll learn how to: Identify strategic ambitions and challenges using listening and mapping tours Visualize your business landscape and crucial capabilities with Wardley Mapping Create a product taxonomy as a framework for your architecture Run big picture EventStorming workshops to map business domains Apply Team Topologies patterns to identify and refine value streams Design loosely coupled, domain-aligned software architectures Build internal developer platforms for rapid, reliable evolution Implement data mesh principles and tools to revolutionize data engineering Deliver compelling modernization roadmaps focused on continuous value Architecture Modernization: Socio-technical alignment of software, strategy, and structure shows you how to turn the practice of architecting systems into a transformative process for your entire company. Chapter-by-chapter, you’ll identify the reasons and benefits of modernization, design an architecture that works for your business, and then implement your new approach in a progressive and sustainable manner. Every technique is illustrated with insightful industry examples and an interactive Miro board that lets you dig deeper. Forewords by Matthew Skelton and Xin Yao. About the technology The decisions you make about your software are inherently connected to the decisions you make about your business. Why not turn the mundane task of modernizing legacy systems into a transformative process for your entire company? This book shows you how! It reveals a socio-technical approach to align your software and products with organizational dynamics and ways of working. About the book Architecture Modernization: Socio-technical alignment of software, strategy, and structure presents a clear path for upgrading your entire organization when you re-imagine your software. In it, you’ll learn to combine practices like Domain-Driven Design, Event Storming, and Wardley Mapping to discover user needs, design optimal architecture, and avoid falling back into old habits. Provocative examples from Danske, Salesforce, the UK Government, and others show the real-world result of each approach, identifying techniques you can apply effectively in your own business. About the reader For CTOs, tech leads, and principal engineers who decide on architecture and organization design. About the author Nick Tune helps organizations modernize their architectures through empowered product teams and continuous delivery. Jean-Georges Perrin builds innovative and modern data platforms. The technical editor on this book was Kamil Nicieja.

Get the eBook free when you register your print book at Manning.Build AI agents that do more than respond. Give them tools, connect them to knowledge, coordinate their work, and evaluate how well they perform. AI Agents in Action, Second Edition teaches you how to design, build, evaluate, and deploy AI agents through working Python examples. Youll progress from a simple agent to tool-using assistants, coordinated multi-agent workflows, and deployable systems. Along the way, youll learn how the pieces fit together and where to look when an agent needs improvement. Whats new and included in the second edition? A near-total rewrite with new and extensive coverage of the latest agent innovations Agents start to finishfrom prompting and planning, to memory and evaluation New chapter! Evaluate agents with LLM-as-judge, test-driven-development, and observable behavior New chapter! Explore the agentic loop, build task-focused agents, and develop long-horizon, goal focused systems New chapter! Metacognition harnesses that structure agent thinking, planning, and learning New chapter! Practical tips and techniques for building AI applications, help desks, and deep research agents New chapter! Voice-driven AI agents, including real-world deployment patterns for multi-agent systems Multi-agent workflows with assembly-flow, orchestration, and collaboration patterns Hands-on with the Model Context Protocol (MCP) to connect agents to tools and servers Hybrid search for Retrieval Augmented Generation (RAG) and practical ways to use memory in agents With more than 95% rewritten from the first edition, AI Agents in Action is far more than a refresh! Youll go right from your agents first response, to systems that use tools, work together, draw on knowledge, and incorporate feedback. All illustrated with practical examples throughout. What's inside Autonomous agent design and deployment MCP-based tools, resources, prompts, memory, and server integrations Reasoning and planning patterns including ReAct, Reflexion, Tree-of- Thought, and Sequential Thinking About the reader For intermediate Python programmers. No experience with AI agents and agentic systems required. About the author Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Mannings Evolutionary Deep Learning.

Quickly master the massive Spring ecosystem with this focused, hands-on guide that teaches you exactly what you need to know.In Spring Start Here, you will learn how to:     Build web applications with Spring     Manage application objects with Spring context     Implement data persistence using data sources and transactions     Implement data exchange between applications using REST services     Utilize Spring Boot's convention-over-configuration approach     Write unit and integration tests for apps implemented with Spring     Minimize work when building any kind of app     Persisting data in a Spring application using the latest approach Spring Start Here introduces you to Java development with Spring by concentrating on the core concepts you'll use in every application you build. You'll learn how to refactor an existing application to Spring, how to use Spring tools to make SQL database requests and REST calls, and how to secure your projects with Spring Security. There's always more to learn, and this book will make your next steps much easier. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology For Java developers, Spring is the must-learn framework. This incredible development tool powers everything from small business ecommerce applications to enterprise-scale microservices. Mastering Spring is a long journey. Taking your first step is easy! Start here. About the book Spring Start Here teaches Java developers how to build applications using Spring framework. Informative graphics, relevant examples, and author Laurentiu Spilca's clear and lively writing make it easy to pick up the skills you need. You'll discover how to plan, write, and test applications. And by concentrating on the most important features, this no-nonsense book gives you a firm foundation for exploring Spring's rich ecosystem. What's inside     Build web applications with Spring     Minimize repetition and manual work     Persisting data in a Spring application     HTTP and REST-based web services     Testing your Spring implementations About the reader For readers with beginning to intermediate Java skills. About the author Lauren?iu Spilca is a skilled Java and Spring developer and an experienced technology instructor. Table of Contents PART 1 FUNDAMENTALS 1 Spring in the real world 2 The Spring context: Defining beans 3 The Spring context: Wiring beans 4 The Spring context: Using abstractions 5 The Spring context: Bean scopes and life cycle 6 Using aspects with Spring AOP PART 2 IMPLEMENTATION 7 Understanding Spring Boot and Spring MVC 8 Implementing web apps with Spring Boot and Spring MVC 9 Using the Spring web scopes 10 Implementing REST services 11 Consuming REST endpoints 12 Using data sources in Spring apps 13 Using transactions in Spring apps 14 Implementing data persistence with Spring Data 15 Testing your Spring app

Get the eBook free when you register your print book at Manning."e;An exceptional deep dive into the next frontier of AI.Aman Chadha, GoogleThis book is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-trained LLM, learn how text generation works, build reliable evaluation tools, improve reasoning through inference-time methods, then move into training-based approaches such as reinforcement learning and distillation.The progression is deliberate. Early chapters establish the baseline model and explain text generation, KV caching, and evaluation with math verifiers. The middle chapters show how reasoning can be improved without changing model weights, using chain-of-thought prompting, sampling, self-consistency, response scoring, and self-refinement. Later chapters move to changing the model itself through reinforcement learning with verifiable rewards, GRPO improvements, format rewards, and finally distillation from stronger reasoning models into smaller ones.The book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls. Readers see how self-consistency, self-refinement, Best-of-N, and training-based methods actually work, including their cost and latency trade-offs. It also discusses common failure modes, including cases where refinement can make answers worse. Difficult concepts such as softmax, temperature, and top-p sampling are clarified with code-linked explanations and diagrams, and visual workflows make pipelines and scoring methods easier to follow.Reading the book feels like following a guided technical build rather than a loose survey of AI topics. Each concept is introduced because the project now needs it. Diagrams, roadmaps, code listings, exercises, and repeated workflow summaries help readers stay oriented through advanced material. This structure reflects Sebastian Raschkas professional strength: explaining complex machine learning topics by making every detail concrete and showing exactly where each section fits in the larger story. He does not treat mechanisms like evaluation, log-probabilities, KL regularization, or distillation as isolated abstractions; he connects them to the goal of making reasoning models understandable and implementable.Physically and organizationally, the book has eight chapters and seven substantial appendixes. That design keeps the main narrative focused while moving supporting material like references, exercise solutions, model source code, larger models, batching, evaluation alternatives, and chat interfaces into ordered appendixes. The result is a logically flowing book that remains hands-on, navigable, and technically deep without constantly interrupting the central build.What's insideFrom-scratch implementations of core LLM reasoning improvementsVerifier-based evaluation methodsRL with automatic verifiers for mathematics tasksAbout the readerFor readers who know Python and have some knowledge of machine learning.About the authorSebastian Raschka is an LLM Research Engineer with over a decade of experience. He is the author of the bestselling book Build a Large Language Model (From Scratch).

Practical techniques to accelerate software development using generative AI.Lets get real. Youd like to hand off a lot of tedious software development tasks to an assistantand now you can! AI-powered coding tools like Copilot can accelerate research, design, code creation, testing, troubleshooting, documentation, refactoring and more. Coding with AI shows you how. Written for working developers, this book fast-tracks you to AI-powered productivity with bite-size projects, tested prompts, and techniques for getting the most out of AI.In Coding with AI youll learn how to:Incorporate AI tools into your development workflowCreate pro-quality documentation and testsDebug and refactor software efficientlyCreate and organize reusable promptsCoding with AI takes you through several small Python projects with the help of AI tools, showing you exactly how to use AI to create and refine real software. This book skips the baby steps and goes straight to the techniques youll use on the job, every day. Youll learn to sidestep AI inefficiencies like hallucination and identify the places where AI can save you the most time and effort.About the technologyTaking a systematic approach to coding with Al will deliver the clarity, consistency, and scalability you need for production-grade applications. With practice, you can use AI tools to break down complex problems, generate maintainable code, enhance your models, and streamline debugging, testing, and collaboration. As you learn to work with AIs strengthsand recognize its limitationsyoull build more reliable software and find that the quality of your generated code improves significantly.About the bookCoding with AI shows you how to gain massive benefits from a powerful array of AI-driven development tools and techniques. And it shares the insights and methods you need to use them effectively in professional projects. Following realistic examples, youll learn AI coding for database integration, designing a UI, and establishing an automated testing suite. Youll even vibe code a gamebut only after youve built a rock-solid foundation.What's insideIncorporate AI into your development workflowCreate pro-quality documentation and testsDebug and refactor software efficientlyCreate and organize reusable promptsAbout the readerFor professional software developers. Examples in Python.About the authorJeremy C. Morgan has two decades of experience as an engineer building software for everything from Fortune 100 companies to tiny startups.Table of ContentsPart 11 Introducing generative AI2 First steps with AI-assisted codingPart 23 Design and discovery4 Coding the first version of our application5 Using Blackbox AI to generate base code6 Generating a software backend with TabninePart 37 Building user interfaces with ChatGPT8 Building effective tests with generative AI9 Prompt engineering10 Vibe coding with Cursor

If you need to learn Spring, look no further than this widely beloved and comprehensive guide! Fully revised for Spring 5.3, and packed with interesting real-world examples to get your hands dirty with Spring.In Spring in Action, 6th Edition you will learn:     Building reactive applications     Relational and NoSQL databases     Integrating via HTTP and REST-based services, and sand reactive RSocket services     Reactive programming techniques     Deploying applications to traditional servers and containers     Securing applications with Spring Security Over the years, Spring in Action has helped tens of thousands of developers get a major productivity boost from Spring. This new edition of the classic bestseller covers all of the new features of Spring 5.3 and Spring Boot 2.4 along with examples of reactive programming, Spring Security for REST Services, and bringing reactivity to your databases. You'll also find the latest Spring best practices, including Spring Boot for application setup and configuration. About the technology Spring is required knowledge for Java developers! Why? Th is powerful framework eliminates a lot of the tedious configuration and repetitive coding tasks, making it easy to build enterprise-ready, production-quality software. The latest updates bring huge productivity boosts to microservices, reactive development, and other modern application designs. It’s no wonder over half of all Java developers use Spring. About the book Spring in Action, Sixth Edition is a comprehensive guide to Spring’s core features, all explained in Craig Walls’ famously clear style. You’ll put Spring into action as you build a complete database-backed web app step-by-step. This new edition covers both Spring fundamentals and new features such as reactive flows, Kubernetes integration, and RSocket. Whether you’re new to Spring or leveling up to Spring 5.3, make this classic bestseller your bible! What's inside     Relational and NoSQL databases     Integrating via RSocket and REST-based services     Reactive programming techniques     Deploying applications to traditional servers and containers About the reader For beginning to intermediate Java developers. About the author Craig Walls is an engineer at VMware, a member of the Spring engineering team, a popular author, and a frequent conference speaker. Table of Contents PART 1 FOUNDATIONAL SPRING 1 Getting started with Spring 2 Developing web applications 3 Working with data 4 Working with nonrelational data 5 Securing Spring 6 Working with configuration properties PART 2 INTEGRATED SPRING 7 Creating REST services 8 Securing REST 9 Sending messages asynchronously 10 Integrating Spring PART 3 REACTIVE SPRING 11 Introducing Reactor 12 Developing reactive APIs 13 Persisting data reactively 14 Working with RSocket PART 4 DEPLOYED SPRING 15 Working with Spring Boot Actuator 16 Administering Spring 17 Monitoring Spring with JMX 18 Deploying Spring

This international bestseller has been revised with new exercises, annotations, and full coverage of Scala 3.In Functional Programming in Scala, Second Edition you will learn how to:   Recognize and write purely functional code Work with errors without using exceptions Work with state and concurrency Interact with functional structures that define common behaviors Write code that performs I/O without sacrificing functional programming Functional Programming in Scala has helped over 30,000 developers discover the power of functional programming. You’ll soon see why reviewers have called it “mindblowing”! The book smooths the complexity curve of functional programming, making it simple to understand the basics and intuitive to progress to more advanced topics. Concrete examples and exercises show you FP in the real world and reveal how it can improve your everyday coding practices. This second edition comes packed with the latest standards of FP, as well as full code updates to Scala 3, and its new language features. Foreword by Daniel Spiewak. About the Technology  Functional code is easy to test, reuse, and parallelize, and it’s practically immune to whole categories of state-related bugs. With its strong functional features, familiar syntax, and seamless interoperability with Java, there’s no better place to start learning functional programming than the flexible Scala language. About the Book  In Functional Programming with Scala, Second Edition you’ll learn functional programming from first principles. Hands-on exercises and examples make it easy to start thinking and coding functionally. This revised edition contains extensive exercise annotations to help you explore FP in depth, along with steps to build your own functional libraries in Scala. Once the functional lightbulb goes on, you’ll never look at coding the same way again. What’s Inside   Recognize and write purely functional code Work with errors without using exceptions Work with state and concurrency Interact with functional structures that define common behaviors About the Reader For Java or Scala programmers. No knowledge of functional programming required. About the Author Michael Pilquist is the lead maintainer of FS2, a functional streaming library, and contributes to the Typelevel ecosystem. Paul Chiusano and Rúnar Bjarnason are recognized experts in functional programming and authors of the first edition of Functional Programming with Scala. Table of Contents:    PART 1 - INTRODUCTION TO FUNCTIONAL PROGRAMMING 1 What is functional programming? 2 Getting started with functional programming in Scala 3 Functional data structures 4 Handling errors without exceptions 5 Strictness and laziness 6 Purely functional state PART 2 - FUNCTIONAL DESIGN AND COMBINATOR LIBRARIES 7 Purely functional parallelism 8 Property-based testing 9 Parser combinators PART 3 - COMMON STRUCTURES IN FUNCTIONAL DESIGN 10 Monoids 11 Monads 12 Applicative and traversable functors PART 4 - EFFECTS AND I/O 13 External effects and I/O 14 Local effects and mutable state 15 Stream processing and incremental I/O

Get the eBook free when you register your print book at Manning.Tens of thousands of developers have learned how to develop and run a Kubernetes environment from Kubernetes in Action. This new second edition updates the definitive guide with new, in-depth coverage of the Kubernetes architecture, including the Kubernetes API, and then ins-and-outs of application deployment.As more and more applications are created using cloud-native and container-based architectures, Kubernetes has become an essential tool for modern developers to master. This book lays out a complete introduction to container technologies and containerized applications along with practical tips for efficient deployment and operation. From building your first cluster, youll steadily expand your initial application, adding features and deepening your knowledge of Kubernetes architecture.In this book, youll find: Up and running with Kubernetes Deploying containers across a cluster Securing clusters Updating applications with zero downtimeAbout the technologyKubernetes is the de facto standard for deploying containerized distributed applications. This powerful platform acts as an operating system for containers and clusters, reducing the need to micro-manage network and server infrastructure. Used daily by millions of developers, administrators, and architects, Kubernetes is must-know enterprise software.About the bookKubernetes in Action, Second Edition is the definitive guide to developing and deploying software with Kubernetes. Built on the bestselling first edition, this end-to-end revision provides up-to-date coverage on structuring Kubernetes-based applications and taking advantage of the Kubernetes API. Youll also discover other key topics like configuration, attaching storage, scaling, and zero-downtime maintenance.What's inside Up and running with Kubernetes Deploying containers across a cluster Updating applications with zero downtimeAbout the readerWritten for intermediate software developers. No prior experience with Kubernetes or containers is required.About the authorMarko Luka is an independent software engineer specializing in cloud-native technologies. Kevin Conner is a Senior Principal Software Engineer at Red Hat, focussed on cloud technologies and software supply chain security.Table of ContentsPart 11 Introducing Kubernetes2 Understanding containers and containerized applications3 Deploying your first application on Kubernetes4 Navigating the Kubernetes API and object modelPart 25 Running applications with pods6 Managing the pod life cycle and container health7 Organizing pods and other resources using namespaces and labelsPart 38 Configuring applications with ConfigMaps and Secrets9 Adding volumes for storage, configuration, and metadata10 Persisting data with PersistentVolumesPart 411 Exposing pods with services12 Using Ingress to route traffic to services13 Routing traffic using the Gateway APIPart 514 Scaling and maintaining pods with ReplicaSets15 Automating application updates with Deployments16 Handling stateful applications with StatefulSets17 Deploying per-node workloads with DaemonSets18 Batch processing with Jobs and CronJob

Whether you know Python or not, this book will help you write amazing Python code using the latest versions of Copilot or ChatGPT.Once, to be a programmer you had to write every line of code yourself. Now tools like GitHub Copilot can instantly generate working programs based on your description in plain English. An instant bestseller, Learn AI-Assisted Python Programming has taught thousands of aspiring programmers how to write Python the easy way—with the help of AI. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming. In Learn AI-Assisted Python Programming, Second Edition you’ll learn how to: • Write fun and useful Python applications—no programming experience required! • Use the GitHub Copilot AI coding assistant to create Python programs • Write prompts that tell Copilot exactly what to do • Read Python code and understand what it does • Test your programs to make sure they work the way you want them to • Fix code with prompt engineering or human tweaks • Apply Python creatively to help out on the job AI moves fast, and so the new edition of Learn AI-Assisted Python Programming, Second Edition is fully updated to take advantage of the latest models and AI coding tools. Written by two esteemed computer science university professors, it teaches you everything you need to start programming Python in an AI-first world. You’ll learn skills you can use to create working apps for data analysis, automating tedious tasks, and even video games. Plus, in this new edition, you’ll find groundbreaking techniques for breaking down big software projects into smaller tasks AI can easily achieve. Foreword by Beth Simon. About the technology The way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly. About the book This book shows you how to create and improve Python programs using AI—even if you’ve never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you’ll even learn enough of the Python language to understand and improve what your AI assistant creates. What''s inside • Prompts for working code • Tweak code manually and with AI help • AI-test your programs • Let AI handle tedious details About the reader If you can move files around on your computer and install new programs, you can learn to write useful software! About the author Dr. Leo Porter is a Teaching Professor at UC San Diego. Dr. Daniel Zingaro is an Associate Teaching Professor at the University of Toronto. The technical editor on this book was Peter Morgan. Table of Contents 1 Introducing AI-assisted programming with GitHub Copilot 2 Getting started with Copilot 3 Designing functions 4 Reading Python code: Part 1 5 Reading Python code: Part 2 6 Testing and prompt engineering 7 Problem decomposition 8 Debugging and better understanding your code 9 Automating tedious tasks 10 Making some games 11 Creating an authorship identification program 12 Future directions

This hands-on beginners guide gets you building games fast, all with the awesome Unity engine! You’ll speed past the basics and use your existing coding skills to create 2D, 3D, and AR/VR games.In Unity in Action, Third Edition, you will learn how to:     Create characters that run, jump, and bump into things     Build 3D first-person shooters and third-person action games     Construct 2D card games and side-scrolling platformers     Script enemies with AI     Improve game graphics by importing models and images     Design an intuitive user interface for your games     Play music and spatially-aware sound effects     Connect your games to the internet for online play     Deploy your games to desktop, mobile, and the web Thousands of new game developers have chosen Joe Hocking’s Unity in Action as their first step toward Unity mastery. Starting with the initial groundwork of a new game development project, you’ll quickly start writing custom code instead of clicking together premade scripts. This fully updated third edition comes packed with fully refreshed graphics, Unity’s latest features, and coverage of augmented and virtual reality toolkits. You'll master the Unity toolset from the ground up, learning the skills to go from application coder to game developer. Foreword by Jesse Schell. About the technology Writing games is rewarding and fun—and with Unity, it’s easy to get started! Unity handles the heavy lifting, so you can focus on game play, graphics, and user experience. C# support and a huge ecosystem of prebuilt components help even first-time developers go from the drawing board to the screen fast. About the book Unity in Action, Third Edition teaches you to create games with the Unity game platform. It’s many 2D, 3D, and AR/VR game examples give you hands-on experience with Unity’s workflow tools and state-of-the-art rendering engine. This fully updated third edition presents new coverage of Unity’s XR toolkit and shows you how you can start building with virtual and augmented reality. What's inside     Create characters that run, jump, and bump into things     Script enemies with AI     Play music and spatially-aware sound effects     Deploy your games to desktop, mobile, and the web About the reader For programmers who know any object-oriented programming language. Examples are in C#. About the author Joe Hocking is a software engineer and Unity expert specializing in interactive media development. Table of Contents PART 1 FIRST STEPS 1 Getting to know Unity 2 Building a demo that puts you in 3D space 3 Adding enemies and projectiles to the 3D game 4 Developing graphics for your game PART 2 GETTING COMFORTABLE 5 Building a Memory game using Unity’s 2D functionality 6 Creating a basic 2D platformer 7 Putting a GUI onto a game 8 Creating a third-person 3D game: Player movement and animation 9 Adding interactive devices and items within the game PART 3 STRONG FINISH 10 Connecting your game to the internet 11 Playing audio: Sound effects and music 12 Putting the parts together into a complete game 13 Deploying your game to players’ devices

Learn how to quickly work out what your Java code is doing so you can understand when and why it misbehaves.Searching for bugs, detangling messy legacy code, or evaluating your codebase for new features sucks up much of a developer's time. Effectively reading and understanding existing code is a true superpower! Troubleshooting Java, Second Edition teaches you to efficiently analyze and debug Java applications using time-tested tools and new AI-powered techniques.In Troubleshooting Java, Second Edition youll learn:Efficiently use debuggers to quickly understand complex codeUtilize AI-driven tools to automate log analysis, trace execution, and predict problemsCorrectly implement and interpret application logsIdentify performance bottlenecks and optimize slow execution pathsProfile SQL queries to diagnose and optimize database interactionsEvaluate heap dumps to detect and fix memory leaksAnalyze thread dumps to resolve deadlocks and concurrency issuesNavigate the intricacies of service-oriented and microservices systemsIn Troubleshooting Java, Second Edition Java veteran Laurentiu Spilca shares techniques for reading, profiling, debugging, and optimizing Java code that hes honed over years working with high-stakes systems. This fully-revised second edition, updates coverage for new Java tools and adds methods for saving time and effort using AI. Youll master techniques to identify memory leaks, optimize database queries, and resolve threading issues with ease. The code samples are in Java, but the skills you learn in this guide can be applied to any JVM language.About the technologyWhether youre working with legacy systems or using AI to generate fresh programs, youll spend more time reading and fixing code than writing it from scratch. Troubleshooting is a messy art, and efficient profiling, logging, debugging, and optimization will save you hours of time and countless headaches.About the bookTroubleshooting Java, Second Edition teaches practical techniques to diagnose, debug, and optimize Java applications. Youll learn to uncover hidden dependencies, diagnose crashes, and resolve performance issues using tools like IntelliJ IDEA Debugger, VisualVM, and Heap Dump Analyzer. This revised and updated second edition introduces AI-powered troubleshooting tools, along with techniques for modern Java and cloud-native apps.What's insideUse debuggers to quickly understand complex codeAutomate code analysis with AI toolsImplement and interpret application logsIdentify performance bottlenecksAbout the readerFor intermediate Java developers.About the authorLaurentiu Spilca is a skilled Java and Spring developer and an experienced technology instructor. He is the author of Spring Start Here and Spring Security in Action.Table of ContentsPart 11 Starting to know your apps2 Understanding your apps logic through debugging techniques3 Finding problem root causes using advanced debugging techniques4 Making the most of logs: Auditing an apps behaviorPart 25 Identifying resource consumption problems using profiling techniques6 Finding hidden problems using profiling techniques7 Investigating locks in multithreaded architectures8 Investigating deadlocks with thread dumpsPart 39 Profiling memory-related problems10 Investigating memory problems with heap dumps11 Analyzing potential JVM problems with GC logsPart 412 Uncovering system-level failures and service communicationproblems13 Measuring data consistency and transactionsA Tools youll needB Opening a projectC Recommended further readingD Understanding Java threadsE Memory management in Java apps

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python.In Deep Learning with Python, Third Edition youll discover:Deep learning from first principlesThe latest features of Keras 3A primer on JAX, PyTorch, and TensorFlowImage classification and image segmentationTime series forecastingLarge Language modelsText classification and machine translationText and image generationbuild your own GPT and diffusion models!Scaling and tuning modelsWith over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator Franois Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images.About the technologyIn less than a decade, deep learning has changed the worldtwice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator Franois Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider.About the bookDeep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer.What's insideHands-on, code-first learningComprehensive, from basics to generative AIIntuitive and easy math explanationsExamples in Keras, PyTorch, JAX, and TensorFlowAbout the readerFor readers with intermediate Python skills. No previous experience with machine learning or linear algebra required.About the authorFranois Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras.Table of Contents1 What is deep learning?2 The mathematical building blocks of neural networks3 Introduction to TensorFlow, PyTorch, JAX, and Keras4 Classification and regression5 Fundamentals of machine learning6 The universal workflow of machine learning7 A deep dive on Keras8 Image classification9 ConvNet architecture patterns10 Interpreting what ConvNets learn11 Image segmentation12 Object detection13 Timeseries forecasting14 Text classification15 Language models and the Transformer16 Text generation17 Image generation18 Best practices for the real world19 The future of AI20 Conclusions

Designed for busy IT professionals, this innovative guide will take you from the basics to PowerShell proficiency through 25 tutorials you can do in your lunch break.In Learn PowerShell in a Month of Lunches, Fourth Edition you will learn:     Discoverability with the Help system     Background jobs and automation techniques     Simple scripting to automate repetitive tasks     Managing cloud services from major cloud providers     Extending PowerShell with commands     Common syntax and commands cheat sheet Learn PowerShell in a Month of Lunches, Fourth Edition is a task-focused guide for administering your systems using PowerShell. It covers core language features and admin tasks, with each chapter a mini-tutorial you can easily complete in under an hour. Discover how PowerShell works on different operating systems, and start automating tasks so they take just a few seconds to complete. No previous scripting experience required. The book is based on the bestselling Learn Windows PowerShell in a Month of Lunches by community legends Don Jones and Jeffery Hicks. PowerShell team members Travis Plunk and Tyler Leonhardt and Microsoft MVP James Petty have updated this edition to the latest version of PowerShell, including its multi-platform expansion into Linux and macOS. About the technology PowerShell gives you complete command line control over admin tasks like adding users, exporting data, and file management. Whether you’re writing one-liners or building complex scripts to manage cloud resources and CI/CD pipelines, PowerShell can handle the task. And now that PowerShell is truly cross-platform, you don’t have to switch scripting languages when you move between Windows, Linux, and macOS. About the book Learn PowerShell in a Month of Lunches, Fourth Edition is a new edition of the bestseller that introduced PowerShell to over 100,000 readers. With bite-sized lessons and hands-on exercises, this amazing book guides you from your first command to writing and debugging reusable scripts for Windows, Linux, and macOS. Set aside just an hour a day and you’ll soon be tackling increasingly complex automation tasks with PowerShell. What's inside     Discoverability with the Help system     PowerShell on macOS and Linux     Background jobs and automation techniques     Managing cloud services from major cloud providers     Common syntax and commands cheat sheet About the reader No previous experience with PowerShell or Bash required. About the author James Petty is CEO of PowerShell.org and The DevOps Collective, and a Microsoft MVP. Travis Plunk is an engineer on the PowerShell team. Tyler Leonhardt is an engineer on Visual Studio Code. Don Jones and Jeffery Hicks are the original authors of Learn Windows PowerShell in a Month of Lunches.

Unlock the groundbreaking advances of deep learning with this extensively revised edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.In Deep Learning with Python, Second Edition you will learn:     Deep learning from first principles     Image classification & image segmentation     Timeseries forecasting     Text classification and machine translation     Text generation, neural style transfer, and image generation Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach, even if you have no background in mathematics or data science.  About the book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp illustrations, and clear examples. You’ll pick up the skills to start developing deep-learning applications. What's inside     Deep learning from first principles     Image classification and image segmentation     Time series forecasting     Text classification and machine translation     Text generation, neural style transfer, and image generation About the reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Table of Contents 1  What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for timeseries 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions

Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.In Deep Learning Patterns and Practices you will learn:     Internal functioning of modern convolutional neural networks     Procedural reuse design pattern for CNN architectures     Models for mobile and IoT devices     Assembling large-scale model deployments     Optimizing hyperparameter tuning     Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside     Modern convolutional neural networks     Design pattern for CNN architectures     Models for mobile and IoT devices     Large-scale model deployments     Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline

Empower your team with platforms built on top of Kubernetes using open source tools.Adopting Kubernetes is complex—especially when you’re working in an organization with multiple teams, deploying to multiple cloud providers, and working with different stacks. Platform Engineering on Kubernetes shows you how to solve these common cloud native problems with open-source tools and emerging best practices from the Kubernetes community. In Platform Engineering on Kubernetes you will learn about: The principles behind platform engineering and how these apply to Kubernetes Evaluating and adopting open-source projects to build domain specific platforms Creating Platform APIs to enable teams to release more software more efficiently Reducing the cognitive load of a platform for your teams Measuring your platform initiatives using established software delivery metrics Package, version, distribute, and deploy with Helm, Tekton, Dagger and Argo CD Implement a multi-cloud infrastructure strategy using Crossplane Progressive upgrades with Knative Serving and Argo Rollouts Enable development teams with standard application-level APIs with Dapr A platform helps your team stay focused on delivering amazing software. But building a reliable platform on top of Kubernetes demands real expertise. Platform Engineering on Kubernetes reveals how to combine multiple popular open-source projects into a custom platform that works for your applications and your teams. It’s the perfect guide for your organization’s journey to Kubernetes, simplifying cloud native development for your dev teams and helping them deliver software faster. Foreword by Jared Watts. About the technology Kubernetes is an amazing orchestration tool, but it’s just the start of your journey to the cloud. To efficiently deliver cloud-native software, your team needs a solid build pipeline, an efficient package manager and distribution mechanism, and APIs that reduce your team’s cognitive load. This book will show you how to build custom platforms on top of Kubernetes—all with open-source tools such as Dapr, Knative, Argo CD and Rollouts, and Tekton. About the book Platform Engineering on Kubernetes starts by clearly defining the elements of a great Kubernetes-based platform. Then, it systematically introduces the tools you’ll need to build a platform that exactly matches your organization’s requirements. Hands-on examples and detailed code guide you through each step. By the end, you’ll be able to create a complete platform to efficiently deliver cloud-native software without being tied to a specific cloud provider or vendor. About the reader For developers and software architects familiar with the basics of containers and Kubernetes. About the author Mauricio Salatino is currently a Dapr OSS Contributor, a Knative Steering Committee member, and co-lead of the Knative Functions working group. Table of Contents 1 (The rise of) platforms on top of Kubernetes 2 Cloud-native application challenges 3 Service pipelines: Building cloud-native applications 4 Environment pipelines: Deploying cloud-native applications 5 Multi-cloud (app) infrastructure 6 Let’s build a platform on top of Kubernetes 7 Platform capabilities I: Shared application concerns 8 Platform capabilities II: Enabling teams to experiment 9 Measuring your platforms

Get the eBook free when you register your print book at Manning."e;A perspective the field has needed. Sutskevers List delivers it with care and historical accuracy.Yanping Huang, GoogleSutskevers List is a guided intellectual journey through the ideas that made modern AI suddenly possible. Each chapter is anchored in specific papers, books, or other sources from Sutskevers list. The papers themselves are not the focus. Instead, the author uses them as entry points into the larger breakthroughs, arguments, interconnections, and shifts in thinking that transformed the field.It begins with AlexNet, where data, GPUs, and training craft made neural networks impossible to dismiss, then moves to ResNet, where depth becomes a superpower rather than a liability. From there, the story accelerates through sequence models, speech systems, attention, Transformers, and hyperscale, showing how AI escaped older bottlenecks and became built to grow.Later chapters ask whether these systems can reason, why simplicity can emerge from complexity, and what intelligence and safety mean once AI capabilities begin to feel uncanny. Reviewers praise Heimanns exquisitely deep, detailed, and nuanced knowledge and the massive amount of gold material gathered here. Yet the book remains remarkably easy to read, turning difficult papers into a guided initiation those papers were never designed to provide on their own.As you go, youll understand how abstract lab results have translated into real-world consequences, including shifting architectures and internal organizational politics. With lucid explanations of the core technologies of AI as defined in Sutskevers collection of seminal papers, Heimann explores common engineering choices, evaluating the strengths and limits of deep learning without falling for hype or cynicism. Complex concepts are clarified through relevant examples, vivid anecdotes, and practical engineering insights.Each of the core papers examined in Sutskevers List represents a crucial steppingstone in the evolution of the AI. Youll love how Richard Heimann combines a deep technical background with a journalistic eye, never losing sight of practical considerations and providing a stepping off point to understand where the technology goes next.Sutskevers List features nine chapters, an epilogue, and a practical appendix, smoothly blending technical instruction with cultural and historical context. The result is a logically flowing book that remains highly accessible, navigable, and technically deep without requiring the reader to have a specialists background.What's inside Decoding landmark AI papers from AlexNet to transformers Understanding scaling laws, reasoning models, and AI safety Engineering patterns that scale from research to real-world systemsAbout the readerFor anyone interested in modern AI and deep learning. No specialist knowledge required.About the authorRichard Heimann has honed his deep AI and machine learning expertise across technical and strategic roles in industry, academia, and government. He excels at translating complex ideas into clear, engaging insights for audiences from practitioners to policymakers.Table of Contents1 What did Ilya see?2 The AlexNet moment3 ResNet revolution4 Deep learning accelerates5 Attention is all you need6 The birth of hyperscale7 The pivot to reasoning8 Simplicity, hidden in complexity9 Safe superintelligenceEpilogue: The missing piecesAppendix: Design patterns for engineers

Automate complex tasks and processes with PowerShell scripts. This amazing book teaches you how to write, test, and organize high-quality, reusable scripts for Windows, Linux, and cloud-based systems.Learn PowerShell Scripting in a Month of Lunches, Second Edition takes you beyond command-line PowerShell and opens up the amazing world of scripting and automation. In just 27 bite-sized lessons, you’ll learn to write scripts that can eliminate repetitive manual tasks, create custom reusable tools, and build effective pipelines and workflows. In Learn PowerShell Scripting in a Month of Lunches, Second Edition you’ll learn: Setting up a reliable scripting environment Designing functions and scripts Effective pipeline usage Scripting and security Dealing with errors and bugs Source control with git Sharing and publishing scripts Professional-grade scripting practices The PowerShell language lets you write scripts to control nearly every aspect of Windows. Just master a few straightforward scripting skills, and you''ll save yourself from hours of tedious tasks. This revised second edition is fully updated to PowerShell’s latest version, including hands-on examples that perfectly demonstrate modern PowerShell’s cross-platform applications. About the technology You can write PowerShell scripts to automate nearly any admin task on Windows, Linux, and macOS. This book shows you how! In just 27 short lessons you can complete on your lunch break, you’ll learn to create, organize, test, and share scripts and tools that will save you hours of time in your daily work. About the book Learn PowerShell Scripting in a Month of Lunches, Second Edition is a hands-on introduction to PowerShell automation and toolbuilding. Updated for the latest version of PowerShell, this thoroughly revised bestseller teaches you how to write efficient scripts, find and squash bugs, and organize your tools into libraries. Along the way, you’ll even pick up tips for securing and managing Linux and macOS systems. What''s inside Setting up a reliable scripting environment Designing functions and scripts Effective pipeline usage Sharing and publishing scripts About the reader Beginning to intermediate knowledge of PowerShell required. About the author James Petty is CEO of PowerShell.org and The DevOps Collective and a Microsoft MVP. Don Jones and Jeffery Hicks are the authors of the first edition of Learn PowerShell Scripting in a Month of Lunches. Table of Contents PART 1 1 Before you begin 2 Setting up your scripting environment 3 WWPD: What would PowerShell do? 4 Review: Parameter binding and the PowerShell pipeline 5 Scripting language: A crash course 6 The many forms of scripting (and which to choose) 7 Scripts and security PART 2 8 Always design first 9 Avoiding bugs: Start with a command 10 Building a basic function and script module 11 Getting started with advanced functions 12 Objects: The best kind of output 13 Using all the streams 14 Simple help: Making a comment 15 Errors and how to deal with them 16 Filling out a manifest PART 3 17 Changing your brain when it comes to scripting 18 Professional-grade scripting 19 An introduction to source control with Git 20 Pestering your script 21 Signing your script 22 Publishing your script PART 4 23 Squashing bugs 24 Enhancing script output presentation 25 Wrapping up the .NET Framework 26 Storing data—not in Excel! 27 Never the end

Learn the big skills of C programming by creating bite-size projects! Work your way through these 15 fun and interesting tiny challenges to master essential C techniques you’ll use in full-size applications.In Tiny C Projects you will learn how to:     Create libraries of functions for handy use and re-use     Process input through an I/O filter to generate customized output     Use recursion to explore a directory tree and find duplicate files     Develop AI for playing simple games     Explore programming capabilities beyond the standard C library functions     Evaluate and grow the potential of your programs     Improve code to better serve users Tiny C Projects is an engaging collection of 15 small programming challenges! This fun read develops your C abilities with lighthearted games like tic-tac-toe, utilities like a useful calendar, and thought-provoking exercises like encoding and cyphers. Jokes and lighthearted humor make even complex ideas fun to learn. Each project is small enough to complete in a weekend, and encourages you to evolve your code, add new functions, and explore the full capabilities of C. About the technology The best way to gain programming skills is through hands-on projects—this book offers 15 of them. C is required knowledge for systems engineers, game developers, and roboticists, and you can start writing your own C programs today. Carefully selected projects cover all the core coding skills, including storing and modifying text, reading and writing files, searching your computer’s directory system, and much more. About the book Tiny C Projects teaches C gradually, from project to project. Covering a variety of interesting cases, from timesaving tools, simple games, directory utilities, and more, each program you write starts out simple and gets more interesting as you add features. Watch your tiny projects grow into real applications and improve your C skills, step by step. What's inside     Caesar cipher solver: Use an I/O filter to generate customized output     Duplicate file finder: Use recursion to explore a directory tree     Daily greetings: Writing the moon phase algorithm     Lotto pics: Working with random numbers And 11 more fun projects! About the reader For C programmers of all skill levels. About the author Dan Gookin has over 30 years of experience writing about complex topics. His most famous work is DOS For Dummies, which established the entire For Dummies brand. Table of Contents 1 Configuration and setup 2 Daily greetings 3 NATO output 4 Caesarean cipher 5 Encoding and decoding 6 Password generators 7 String utilities 8 Unicode and wide characters 9 Hex dumper 10 Directory tree 11 File finder 12 Holiday detector 13 Calendar 14 Lotto picks 15 Tic-tac-toe

”Simple yet empowering. Kids will be amazed at how quickly they can get productive.” - James McGinn, Bull Valley Key Features Learn to program with Python, a language designed to be easy for beginners Written by father-and-son team Warren and Carter Sande Colorful pictures, clever cartoons, and fun examples Practice questions and exercises Kid-tested and reviewed by professional educators Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About The Book With this book, ANYONE can learn to write useful programs and games in Python. Designed especially for readers 9-16 years old, this book is easy to read and use. Printed in full color, it’s never boring, with hands-on practice and interesting graphics throughout. Hello World! Computer Programming for Kids and Other Beginners, Third Edition introduces the world of computer programming in a clear and fun style. Using Python, a programming language designed to be easy to learn, each engaging lesson teaches skills that apply to any kind of programming. It brings to life the basic concepts of computing—looping, decisions, input and output, graphics, and more. Now in its third edition, this international bestseller has been fully updated to Python 3 and includes a new chapter about how the internet works. What You Will Learn Install Python and get set up for programming Math and data for programming Building GUIs for your programs Creating simple games Adding comments to your code Graphics, sprites, and collision detection Simulate pets and a lunar landing Where to go next on your programming journey This Book Is Written For Like the previous two editions, Hello World! Third Edition is not just for kids. While the tone is light and engaging, it doesn't "talk down" to the reader, and beginners of any age will love its readability and sense of humor. Written by Warren Sande and his son, Carter, it is full of examples that will get you thinking and learning. Reviewed by professional educators, this book is kid-tested and parent-approved. You don't need to know anything about programming to use the book, just the basics of using a computer. If you can start a program and save a file, you can learn to program using this book!

Automate your build, test, and deploy pipelines using GitHub Actions!Continuous delivery (CI/CD) pipelines help you automate the software development process and maximize your team’s efficiency. GitHub Actions in Action teaches you how to build, test, and deploy pipelines in GitHub Actions through hands-on labs and projects. In GitHub Actions in Action you will learn how to: • Create and share GitHub Actions workflows • Automate CI/CD workloads and other GitHub tasks • Secure release pipelines with secrets, variables, and environments • Support compliance frameworks • Create safe and scalable self-hosted runners Written by three Microsoft MVPs and tech reviewed by a Staff DevOps Architect from GitHub, this book delivers the hardworking skills and advice you’ll need to be successful on the job. DevOps engineers will love GitHub Actions in Action’s coverage of reliable methods for Infrastructure-as-Code and automating cloud environments. You’ll follow an extended example application for selling tickets, taking it all the way from initial build to cloud deployment. Foreword by Scott Hanselman. About the technology Believe it or not, CI/CD can be simple! With GitHub Actions, you can automate your entire dev process using just the tools built into GitHub—no external frameworks or complex integrations required. GitHub Actions is secure, reliable, and best of all, easy. This book will get you started. About the book GitHub Actions in Action teaches you how to build automated delivery pipelines in GitHub. You’ll start with simple examples that demonstrate workflow and action basics, and then you’ll dive into platform architecture, security, and workflow runtime details. As you go, you’ll build a full CI/CD pipeline, optimizing for compliance, performance, and costs. You’ll even create shareable actions for the GitHub marketplace. What''s inside • Create and share GitHub Actions workflows • Automate testing and other GitHub tasks • Secure release pipelines with secrets, variables, and environments About the reader For developers and DevOps engineers comfortable with GitHub. About the author Michael Kaufmann is a Microsoft Regional Director and MVP. Rob Bos is an Azure and GitHub Trainer, a Microsoft MVP, a GitHub Star, and a LinkedIn Learning Instructor. Marcel de Vries is a CTO of Xebia Microsoft Services, Microsoft Regional Director, and MVP. The technical editor on this book was James Michael Gousset. Table of Contents Part 1 1 Introduction to GitHub Actions 2 Hands-on: My first Actions workflow 3 Workflows 4 GitHub Actions Part 2 5 Runners 6 Self-hosted runners 7 Managing your self-hosted runners Part 3 8 Continuous integration 9 Continuous delivery 10 Security 11 Compliance 12 Improving workflow performance and costs

SummaryVoice Applications for Alexa and Google Assistant is your guide to designing, building, and implementing voice-based applications for Alexa and Google Assistant. Inside, you''ll learn how to build your own "skills"—the voice app term for actions the device can perform—from scratch.Foreword by Max Amordeluso.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You''ll find registration instructions inside the print book.About the TechnologyIn 2018, an estimated 100 million voice-controlled devices were installed in homes worldwide, and the apps that control them, like Amazon Alexa and Google Assistant, are getting more powerful, with new skills being added every day. Great voice apps improve how users interact with the web, whether they''re checking the weather, asking for sports scores, or playing a game.About the BookVoice Applications for Alexa and Google Assistant is your guide to designing, building, and implementing voice-based applications for Alexa and Google Assistant. You''ll learn to build applications that listen to users, store information, and rely on user context, as you create a voice-powered sleep tracker from scratch. With the basics mastered, you''ll dig deeper into multiuse conversational flow and other more-advanced concepts. Smaller projects along the way reinforce your new techniques and best practices. What''s insideBuilding a call-and-response skillDesigning a voice user interfaceUsing conversational contextGoing multimodalTips and best practicesAbout the ReaderPerfect for developers with intermediate JavaScript skills and basic Node.js skills. No previous experience with voice-first platforms is required.About the AuthorDustin A. Coates is a developer who focuses on voice and conversational applications. He''s currently the voice search lead at Algolia and is also a Google Developers Expert for Assistant as well as cohost of the VUX World podcast.Table of ContentsIntroduction to voice first Building a call-and-response skill on Alexa Designing a voice user interface Using entity resolution and built?in intents in Alexa skills Making a conversational Alexa skill VUI and conversation best practices Using conversation tools to add meaning and usabilityDirecting conversation flowBuilding for Google Assistant Going multimodal Push interactions Building for actions on Google with the Actions SDK

Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you’ll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light.Summary Using Fortran, early and accurate forecasts for hurricanes and other major storms have saved thousands of lives. Better designs for ships, planes, and automobiles have made travel safer, more efficient, and less expensive than ever before. Using Fortran, low-level machine learning and deep learning libraries provide incredibly easy, fast, and insightful analysis of massive data. Fortran is an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science, and industry. And it's come a long, long way since starting life on IBM mainframes in 1956. Modern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or re-discover—Fortran.. About the technology For over 60 years Fortran has been powering mission-critical scientific applications, and it isn't slowing down yet! Rock-solid reliability and new support for parallel programming make Fortran an essential language for next-generation high-performance computing. Simply put, the future is in parallel, and Fortran is already there. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. What's inside     Fortran's place in the modern world     Working with variables, arrays, and functions     Module development     Parallelism with coarrays, teams, and events     Interoperating Fortran with C About the reader For developers and computational scientists. No experience with Fortran required. About the author Milan Curcic is a meteorologist, oceanographer, and author of several general-purpose Fortran libraries and applications. Table of Contents PART 1 - GETTING STARTED WITH MODERN FORTRAN 1 Introducing Fortran 2 Getting started: Minimal working app PART 2 - CORE ELEMENTS OF FORTRAN 3 Writing reusable code with functions and subroutines 4 Organizing your Fortran code using modules 5 Analyzing time series data with arrays 6 Reading, writing, and formatting your data PART 3 - ADVANCED FORTRAN USE 7 Going parallel with Fortan coarrays 8 Working with abstract data using derived types 9 Generic procedures and operators for any data type 10 User-defined operators for derived types PART 4 - THE FINAL STRETCH 11 Interoperability with C: Exposing your app to the web 12 Advanced parallelism with teams, events, and collectives

Val''s Blog"A tremendously useful field guide specifically written for developers down in the trenches...waiting for the killer solution..."Web users are getting tired of the traditional web experience. They get frustrated losing their scroll position; they get annoyed waiting for refresh; they struggle to reorient themselves on every new page. And the list goes on. With asynchronous JavaScript and XML, known as "Ajax," you can give them a better experience. Once users have experienced an Ajax interface, they hate to go back. Ajax is new way of thinking that can result in a flowing and intuitive interaction with the user.Ajax in Action helps you implement that thinking--it explains how to distribute the application between the client and the server (hint: use a "nested MVC" design) while retaining the integrity of the system. You will learn how to ensure your app is flexible and maintainable, and how good, structured design can help avoid problems like browser incompatibilities. Along the way it helps you unlearn many old coding habits. Above all, it opens your mind to the many advantages gained by placing much of the processing in the browser. If you are a web developer who has prior experience with web technologies, this book is for you. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.