Bøker utgitt av Manning Publications

Knative in Action teaches you to build complex and efficient serverless applications.Summary Take the pain out of managing serverless applications. Knative, a collection of Kubernetes extensions curated by Google, simplifies building and running serverless systems. Knative in Action guides you through the Knative toolkit, showing you how to launch, modify, and monitor event-based apps built using cloud-hosted functions like AWS Lambda. You’ll learn how to use Knative Serving to develop software that is easily deployed and autoscaled, how to use Knative Eventing to wire together disparate systems into a consistent whole, and how to integrate Knative into your shipping pipeline. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology With Knative, managing a serverless application’s full lifecycle is a snap. Knative builds on Kubernetes orchestration features, making it easy to deploy and run serverless apps. It handles low-level chores—such as starting and stopping instances—so you can concentrate on features and behavior. About the book Knative in Action teaches you to build complex and efficient serverless applications. You’ll dive into Knative’s unique design principles and grasp cloud native concepts like handling latency-sensitive workloads. You’ll deliver updates with Knative Serving and interlink apps, services, and systems with Knative Eventing. To keep you moving forward, every example includes deployment advice and tips for debugging. What's inside     Deploy a service with Knative Serving     Connect systems with Knative Eventing     Autoscale responses for different traffic surges     Develop, ship, and operate software About the reader For software developers comfortable with CLI tools and an OO language like Java or Go. About the author Jacques Chester has worked in Pivotal and VMWare R&D since 2014, contributing to Knative and other projects. Table of Contents 1 Introduction 2 Introducing Knative Serving 3 Configurations and Revisions 4 Routes 5 Autoscaling 6 Introduction to Eventing 7 Sources and Sinks 8 Filtering and Flowing 9 From Conception to Production

In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors.Summary Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you’ll maximize performance no matter which cloud vendor you use. About the book In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. What's inside     Best practices for structured and unstructured data sets     Cloud-ready machine learning tools     Metadata and real-time analytics     Defensive architecture, access, and security About the reader For data professionals familiar with the basics of cloud computing, and Hadoop or Spark. About the author Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. Table of Contents 1 Introducing the data platform 2 Why a data platform and not just a data warehouse 3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google 4 Getting data into the platform 5 Organizing and processing data 6 Real-time data processing and analytics 7 Metadata layer architecture 8 Schema management 9 Data access and security 10 Fueling business value with data platforms

Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together.In Real-world Natural Language Processing you will learn how to:     Design, develop, and deploy useful NLP applications     Create named entity taggers     Build machine translation systems     Construct language generation systems and chatbots     Use advanced NLP concepts such as attention and transfer learning Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps, including chatbots, language detectors, and text classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! Th e field of Natural Language Processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assistants, text generation, and more. About the book Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you’ll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you’ll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process. What's inside     Design, develop, and deploy useful NLP applications     Create named entity taggers     Build machine translation systems     Construct language generation systems and chatbots About the reader For Python programmers. No prior machine learning knowledge assumed. About the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009. He has interned at Google and Microsoft Research, and worked at Duolingo as a Senior Machine Learning Engineer. He now runs his own research and consulting company. Table of Contents PART 1 BASICS 1 Introduction to natural language processing 2 Your first NLP application 3 Word and document embeddings 4 Sentence classification 5 Sequential labeling and language modeling PART 2 ADVANCED MODELS 6 Sequence-to-sequence models 7 Convolutional neural networks 8 Attention and Transformer 9 Transfer learning with pretrained language models PART 3 PUTTING INTO PRODUCTION 10 Best practices in developing NLP applications 11 Deploying and serving NLP applications

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science.In Data Science Bookcamp you will learn:     Techniques for computing and plotting probabilities     Statistical analysis using Scipy     How to organize datasets with clustering algorithms     How to visualize complex multi-variable datasets     How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside     Web scraping     Organize datasets with clustering algorithms     Visualize complex multi-variable datasets     Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution

The perfect starting point for your journey into Scala and functional programming.Summary In Get Programming in Scala you will learn:     Object-oriented principles in Scala     Express program designs in functions     Use types to enforce program requirements     Use abstractions to avoid code duplication     Write meaningful tests and recognize code smells Scala is a multi-style programming language for the JVM that supports both object-oriented and functional programming. Master Scala, and you'll be well-equipped to match your programming approach to the type of problem you're dealing with. Packed with examples and exercises, Get Programming with Scala is the perfect starting point for developers with some OO knowledge who want to learn Scala and pick up a few FP skills along the way. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Scala developers are in high demand. This flexible language blends object-oriented and functional programming styles so you can write flexible, easy-to-maintain code. Because Scala runs on the JVM, your programs can interact seamlessly with Java libraries and tools. If you’re comfortable writing Java, this easy-to-read book will get you programming with Scala fast. About the book Get Programming with Scala is a fast-paced introduction to the Scala language, covering both Scala 2 and Scala 3. You’ll learn through lessons, quizzes, and hands-on projects that bring your new skills to life. Clear explanations make Scala’s features and abstractions easy to understand. As you go, you’ll learn to write familiar object-oriented code in Scala and also discover the possibilities of functional programming. What's inside     Apply object-oriented principles in Scala     Learn the core concepts of functional programming     Use types to enforce program requirements     Use abstractions to avoid code duplication     Write meaningful tests and recognize code smells About the reader For developers who know an OOP language like Java, Python, or C#. No experience with Scala or functional programming required. About the author Daniela Sfregola is a Senior Software Engineer and a Scala user since 2013. She is an active contributor to the Scala Community, a public speaker at Scala conferences and meetups, and a maintainer of open-source projects. Table of Contents Unit 0 HELLO SCALA! Unit 1 THE BASICS Unit 2 OBJECT-ORIENTED FUNDAMENTALS Unit 3 HTTP SERVER Unit 4 IMMUTABLE DATA AND STRUCTURES Unit 5 LIST Unit 6 OTHER COLLECTIONS AND ERROR HANDLING Unit 7 CONCURRENCY Unit 8 JSON (DE)SERIALIZATION

If you're browsing the web, using public APIs, making and receiving electronic payments, registering and logging in users, or experimenting with blockchain, you're relying on cryptography. And you're probably trusting a collection of tools, frameworks, and protocols to keep your data, users, and business safe. It's important to understand these tools so you can make the best decisions about how, where, and why to use them. Real-World Cryptography teaches you applied cryptographic techniques to understand and apply security at every level of your systems and applications.about the technologyCryptography is the foundation of information security. This simultaneously ancient and emerging science is based on encryption and secure communication using algorithms that are hard to crack even for high-powered computer systems. Cryptography protects privacy, secures online activity, and defends confidential information, such as credit cards, from attackers and thieves. Without cryptographic techniques allowing for easy encrypting and decrypting of data, almost all IT infrastructure would be vulnerable.about the bookReal-World Cryptography helps you understand the cryptographic techniques at work in common tools, frameworks, and protocols so you can make excellent security choices for your systems and applications. There's no unnecessary theory or jargon-just the most up-to-date techniques you'll need in your day-to-day work as a developer or systems administrator. Cryptography expert David Wong takes you hands-on with cryptography building blocks such as hash functions and key exchanges, then shows you how to use them as part of your security protocols and applications. Alongside modern methods, the book also anticipates the future of cryptography, diving into emerging and cutting-edge advances such as cryptocurrencies, password-authenticated key exchange, and post-quantum cryptography. Throughout, all techniques are fully illustrated with diagrams and real-world use cases so you can easily see how to put them into practice. what's insideBest practices for using cryptographyDiagrams and explanations of cryptographic algorithmsIdentifying and fixing cryptography bad practices in applicationsPicking the right cryptographic tool to solve problemsabout the readerFor cryptography beginners with no previous experience in the field.about the authorDavid Wong is a senior engineer working on Blockchain at Facebook. He is an active contributor to internet standards like Transport Layer Security and to the applied cryptography research community. David is a recognized authority in the field of applied cryptography; he's spoken at large security conferences like Black Hat and DEF CON and has delivered cryptography training sessions in the industry.

Functional Programming in Kotlin is a reworked version of the bestselling Functional Programming in Scala, with all code samples, instructions, and exercises translated into the powerful Kotlin language. In this authoritative guide, you'll take on the challenge of learning functional programming from first principles, and start writing Kotlin code that's easier to read, easier to reuse, better for concurrency, and less prone to bugs and errors.about the technologyKotlin is a new JVM language designed to interoperate with Java and offer an improved developer experience for creating new applications. It's already a top choice for writing web services, and Android apps. Although it preserves Java's OO roots, Kotlin really shines when you adopt a functional programming mindset. By learning the core principles and practices of functional programming outlined in this book, you'll start writing code that's easier to read, easier to test and reuse, better for concurrency, and less prone to bugs.about the bookFunctional Programming in Kotlin is a serious tutorial for programmers looking to learn FP and apply it to the everyday business of coding. Based on the bestselling Functional Programming in Scala, this book guides intermediate Java and Kotlin programmers from basic techniques to advanced topics in a logical, concise, and clear progression. In it, you'll find concrete examples and exercises that open up the world of functional programming. The book will deliver practical mastery of FP using Kotlin and a valuable perspective on program design that you can apply to other languages. what's insideFunctional programming techniques for real-world applicationsWrite combinator librariesIdentify common structures and idioms in functional designCode for simplicity, modularity, and fewer bugsabout the readerFor intermediate Kotlin and Java developers. No experience with functional programming is required.about the authorMarco Vermeulen has almost two decades of programming experience on the JVM, with much of that time spent on functional programming using Scala and Kotlin.Rúnar Bjarnason and Paul Chiusano are the authors of Functional Programming in Scala, on which this book is based. They are internationally-recognized experts in functional programming and the Scala programming language.

Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system's infrastructure. Following a real-world use case for calculating taxi fares, you'll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware.about the technologyYour new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure. Serverless machine learning offers a productivity-boosting alternative. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, you're free to focus on tuning and improving your models.about the bookCloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You'll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you'll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you'll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you're done, you'll have the tools to easily bridge the gap between ML models and a fully functioning production system. what's insideExtracting, transforming, and loading datasetsQuerying datasets with SQLUnderstanding automatic differentiation in PyTorchDeploying trained models and pipelines as a service endpointMonitoring and managing your pipeline's life cycleMeasuring performance improvementsabout the readerFor data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required.about the authorCarl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world's foremost experts in machine learning and also helped manage the company's efforts to democratize artificial intelligence. You can learn more about Carl from his blog Clouds With Carl.

The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether.Summary The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Keeping customers active and engaged is essential for any business that relies on recurring revenue and repeat sales. Customer turnover—or “churn”—is costly, frustrating, and preventable. By applying the techniques in this book, you can identify the warning signs of churn and learn to catch customers before they leave. About the book Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold’s methods, you’ll reap the benefits of high customer retention. What's inside     Calculating churn metrics     Identifying user behavior that predicts churn     Using churn reduction tactics with customer segmentation     Applying churn analysis techniques to other business areas     Using AI for accurate churn forecasting About the reader For readers with basic data analysis skills, including Python and SQL. About the author Carl Gold (PhD) is the Chief Data Scientist at Zuora, Inc., the industry-leading subscription management platform. Table of Contents: PART 1 - BUILDING YOUR ARSENAL 1 The world of churn 2 Measuring churn 3 Measuring customers 4 Observing renewal and churn PART 2 - WAGING THE WAR 5 Understanding churn and behavior with metrics 6 Relationships between customer behaviors 7 Segmenting customers with advanced metrics PART 3 - SPECIAL WEAPONS AND TACTICS 8 Forecasting churn 9 Forecast accuracy and machine learning 10 Churn demographics and firmographics 11 Leading the fight against churn

Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines.Summary A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You'll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline's needs. What's inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the author Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Table of Contents PART 1 - GETTING STARTED 1 Meet Apache Airflow 2 Anatomy of an Airflow DAG 3 Scheduling in Airflow 4 Templating tasks using the Airflow context 5 Defining dependencies between tasks PART 2 - BEYOND THE BASICS 6 Triggering workflows 7 Communicating with external systems 8 Building custom components 9 Testing 10 Running tasks in containers PART 3 - AIRFLOW IN PRACTICE 11 Best practices 12 Operating Airflow in production 13 Securing Airflow 14 Project: Finding the fastest way to get around NYC PART 4 - IN THE CLOUDS 15 Airflow in the clouds 16 Airflow on AWS 17 Airflow on Azure 18 Airflow in GCP

”This book takes an impossibly broad area of computer science and communicates what working developers need to understand in a clear and thorough way.” - David Jacobs, Product Advance Local Key Features Master the core algorithms of deep learning and AI Build an intuitive understanding of AI problems and solutions Written in simple language, with lots of illustrations and hands-on examples Creative coding exercises, including building a maze puzzle game and exploring drone optimizationAbout The Book “Artificial intelligence” requires teaching a computer how to approach different types of problems in a systematic way. The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies.  Mastering the core algorithms for search, image recognition, and other common tasks is essential to building good AI applications Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts.You’ll explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. All you need is the algebra you remember from high school math class and beginning programming skills.  What You Will Learn Use cases for different AI algorithms Intelligent search for decision making Biologically inspired algorithms Machine learning and neural networks Reinforcement learning to build a better robot This Book Is Written For For software developers with high school–level math skills. About the Author Rishal Hurbans is a technologist, startup and AI group founder, and international speaker. Table of Contents 1 Intuition of artificial intelligence 2 Search fundamentals 3 Intelligent search 4 Evolutionary algorithms 5 Advanced evolutionary approaches 6 Swarm intelligence: Ants 7 Swarm intelligence: Particles 8 Machine learning 9 Artificial neural networks 10 Reinforcement learning with Q-learning

By adopting the micro frontends approach and designing your web apps as systems of features, you can deliver faster feature development, easier upgrades, and pick and choose the technology you use in your stack. Micro Frontends in Action is your guide to simplifying unwieldy frontends by composing them from small, well-defined units. You'll learn to integrate web applications made up of smaller fragments using tools such as web components or server side includes, how to solve the organizational challenges of micro frontends, and how to create a design system that ensures an end user gets a consistent look and feel for your application. Key Features· Applying integration strategies with iframes, AJAX, server-side includes, web components and the app-shell approach· Optimizing for performance and asset delivery strategies· Designing coherent user interfaces· Migrating to a micro frontends architecture For intermediate web developers, team leaders, and software architects.

Discover valuable machine learning techniques you can understand and apply using just high-school math.In Grokking Machine Learning you will learn:     Supervised algorithms for classifying and splitting data     Methods for cleaning and simplifying data     Machine learning packages and tools     Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's inside     Supervised algorithms for classifying and splitting data     Methods for cleaning and simplifying data     Machine learning packages and tools     Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python. No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning

Svelte and Sapper in Action teaches you to design and build fast, elegant web applications. You’ll start immediately by creating an engaging Travel Packing app as you learn to create Svelte components and develop great UX. You’ll master Svelte’s unique state management model, use Sapper for simplified page routing, and take on modern best practices like code splitting, offline support, and server-rendered views.Summary Imagine web apps with fast browser load times that also offer amazing developer productivity and require less code to create. That’s what Svelte and Sapper deliver! Svelte pushes a lot of the work a frontend framework would handle to the compile step, so your app components come out as tight, well-organized JavaScript modules. Sapper is a lightweight web framework that minimizes application size through server-rendering front pages and only loading the JavaScript you need. The end result is more efficient apps with great UX and simplified state management. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Many web frameworks load hundreds of “just-in-case” code lines that clutter and slow your apps. Svelte, an innovative, developer-friendly tool, instead compiles applications to very small bundles for lightning-fast load times that do more with less code. Pairing Svelte with the Sapper framework adds features for flexible and simple page routing, server-side rendering, static site development, and more. About the book Svelte and Sapper in Action teaches you to design and build fast, elegant web applications. You’ll start immediately by creating an engaging Travel Packing app as you learn to create Svelte components and develop great UX. You’ll master Svelte’s unique state management model, use Sapper for simplified page routing, and take on modern best practices like code splitting, offline support, and server-rendered views. What's inside - Creating Svelte components - Using stores for shared data - Configuring page routing - Debugging, testing, and deploying Svelte apps - Using Sapper for dynamic and static sites About the reader For web developers familiar with HTML, CSS, and JavaScript. About the author Mark Volkmann is a partner at Object Computing, where he has provided software consulting and training since 1996. Table of Contents PART 1 - GETTING STARTED 1 Meet the players 2 Your first Svelte app PART 2 - DEEPER INTO SVELTE 3 Creating components 4 Block structures 5 Component communication 6 Stores 7 DOM interactions 8 Lifecycle functions 9 Client-side routing 10 Animation 11 Debugging 12 Testing 13 Deploying 14 Advanced Svelte PART 3 - DEEPER INTO SAPPER 15 Your first Sapper app 16 Sapper applications 17 Sapper server routes 18 Exporting static sties with Sapper 19 Sapper offline support PART 4 - BEYOND SVELTE AND SAPPER 20 Preprocessors 21 Svelte Native

React Hooks in Action teaches you to write fast and reusable React components using Hooks.Summary Build stylish, slick, and speedy-to-load user interfaces in React without writing custom classes. React Hooks are a new category of functions that help you to manage state, lifecycle, and side effects within functional components. React Hooks in Action teaches you to use pre-built hooks like useState, useReducer and useEffect to build your own hooks. Your code will be more reusable, require less boilerplate, and you’ll instantly be a more effective React developer. About the technology Get started with React Hooks and you’ll soon have code that’s better organized and easier to maintain. React Hooks are targeted JavaScript functions that let you reuse and share functionality across components. Use them to split components into smaller functions, manage state and side effects, and access React features without classes—all without having to rearrange your component hierarchy. About the book React Hooks in Action teaches you to write fast and reusable React components using Hooks. You’ll start by learning to create component code with Hooks. Next, you’ll implement a resource booking application that demonstrates managing local state, application state, and side effects like fetching data. Code samples and illustrations make learning Hooks easy. What's inside     Build function components that access React features     Manage local, shared, and application state     Explore built-in, custom, and third-party hooks     Load, update, and cache data with React Query     Improve page and data loading with code-splitting and React Suspense About the reader For beginning to intermediate React developers. About the author John Larsen has been a teacher and web developer for over 20 years, creating apps for education and helping students learn to code. He is the author of Get Programming with JavaScript. Table of Contents PART 1 1 React is evolving 2 Managing component state with useState hook 3 Managing component state with useReducer hook 4 Working with side effects 5 Managing component state with useRef hook 6 Managing application state 7 Managing performance with useMemo 8 Managing state with the Context API 9 Creating your own hooks 10 Using third party hooks PART 2 11 Code splitting with Suspense 12 Integrating data-fetching with Suspense 13 Experimenting with useTransition, useDeferredValue and SuspenseList

API Security in Action teaches you how to create secure APIs for any situation. By following this hands-on guide you’ll build a social network API while mastering techniques for flexible multi-user security, cloud key management, and lightweight cryptography.Summary A web API is an efficient way to communicate with an application or service. However, this convenience opens your systems to new security risks. API Security in Action gives you the skills to build strong, safe APIs you can confidently expose to the world. Inside, you’ll learn to construct secure and scalable REST APIs, deliver machine-to-machine interaction in a microservices architecture, and provide protection in resource-constrained IoT (Internet of Things) environments. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology APIs control data sharing in every service, server, data store, and web client. Modern data-centric designs—including microservices and cloud-native applications—demand a comprehensive, multi-layered approach to security for both private and public-facing APIs. About the book API Security in Action teaches you how to create secure APIs for any situation. By following this hands-on guide you’ll build a social network API while mastering techniques for flexible multi-user security, cloud key management, and lightweight cryptography. When you’re done, you’ll be able to create APIs that stand up to complex threat models and hostile environments. What's inside     Authentication     Authorization     Audit logging     Rate limiting     Encryption About the reader For developers with experience building RESTful APIs. Examples are in Java. About the author Neil Madden has in-depth knowledge of applied cryptography, application security, and current API security technologies. He holds a Ph.D. in Computer Science. Table of Contents PART 1 - FOUNDATIONS 1 What is API security? 2 Secure API development 3 Securing the Natter API PART 2 - TOKEN-BASED AUTHENTICATION 4 Session cookie authentication 5 Modern token-based authentication 6 Self-contained tokens and JWTs PART 3 - AUTHORIZATION 7 OAuth2 and OpenID Connect 8 Identity-based access control 9 Capability-based security and macaroons PART 4 - MICROSERVICE APIs IN KUBERNETES 10 Microservice APIs in Kubernetes 11 Securing service-to-service APIs PART 5 - APIs FOR THE INTERNET OF THINGS 12 Securing IoT communications 13 Securing IoT APIs

Vert.x in Action teaches you how to build production-quality reactive applications in Java. This book covers core Vert.x concepts, as well as the fundamentals of asynchronous and reactive programming. Learn to develop microservices by using Vert.x tools for database communications, persistent messaging, and test app resiliency. The patterns and techniques included here transfer to reactive technologies and frameworks beyond Vert.x.Summary As enterprise applications become larger and more distributed, new architectural approaches like reactive designs, microservices, and event streams are required knowledge. The Vert.x framework provides a mature, rock-solid toolkit for building reactive applications using Java, Kotlin, or Scala. Vert.x in Action teaches you to build responsive, resilient, and scalable JVM applications with Vert.x using well-established reactive design patterns. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Vert.x is a collection of libraries for the Java virtual machine that simplify event-based and asynchronous programming. Vert.x applications handle tedious tasks like asynchronous communication, concurrent work, message and data persistence, plus they’re easy to scale, modify, and maintain. Backed by the Eclipse Foundation and used by Red Hat and others, this toolkit supports code in a variety of languages. About the book Vert.x in Action teaches you how to build production-quality reactive applications in Java. This book covers core Vert.x concepts, as well as the fundamentals of asynchronous and reactive programming. Learn to develop microservices by using Vert.x tools for database communications, persistent messaging, and test app resiliency. The patterns and techniques included here transfer to reactive technologies and frameworks beyond Vert.x. What's inside     Building reactive services     Responding to external service failures     Horizontal scaling     Vert.x toolkit architecture and Vert.x testing     Deploying with Docker and Kubernetes About the reader For intermediate Java web developers. About the author Julien Ponge is a principal software engineer at Red Hat, working on the Eclipse Vert.x project. Table of Contents PART 1 - FUNDAMENTALS OF ASYNCHRONOUS PROGRAMMING WITH VERT.X 1 Vert.x, asynchronous programming, and reactive systems 2 Verticles: The basic processing units of Vert.x 3 Event bus: The backbone of a Vert.x application 4 Asynchronous data and event streams 5 Beyond callbacks 6 Beyond the event bus PART 2 - DEVELOPING REACTIVE SERVICES WITHT VERT.X 7 Designing a reactive application 8 The web stack 9 Messaging and event streaming with Vert.x 10 Persistent state management with databases 11 End-to-end real-time reactive event processing 12 Toward responsiveness with load and chaos testing 13 Final notes: Container-native Vert.x

Graph Databases in Action teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine! Key Features· Graph database fundamentals· An overview of the graph database ecosystem· Relational vs. graph database modelling· Querying graphs using Gremlin· Real-world common graph use cases For readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required. About the technology Graph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority. Dave Bechberger has extensive experience using graph databases as a product architect and a consultant. He's spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He's an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences. Josh Perryman is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.

To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting?and lucrative!?careers in some of today's hottest programming fields. Key Features· 2D and 3D vector math· Matrices and linear transformations· Core concepts from linear algebra· Calculus with one or more variables· Algorithms for regression, classification, and clustering· Interesting real-world examples Written for programmers with solid algebra skills (even if they need some dusting off). No formal coursework in linear algebra or calculus is required. About the technology Most businesses realize they need to apply data science and effective machine learning to gain and maintain a competitive edge. To build these applications, they need developers comfortable writing code and using tools steeped in statistics, linear algebra, and calculus. Math also plays an integral role in other modern applications like game development, computer graphics and animation, image and signal processing, pricing engines, and stock market analysis. Paul Orland is CEO of Tachyus, a Silicon Valley startup building predictive analytics software to optimize energy production in the oil and gas industry. As founding CTO, he led the engineering team to productize hybrid machine learning and physics models, distributed optimization algorithms, and custom web-based data visualizations. He has a B.S. in mathematics from Yale University and a M.S. in physics from the University of Washington.