Books published by Manning Publications

”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

Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you'll learn QC fundamentals as you apply quantum programming techniques to real-world examples including cryptography and chemical analysis. Learn Quantum Computing with Python and Q# builds your understanding of quantum computers, using Microsoft's Quantum Development Kit to abstract away the mathematical complexities. You'll learn QC basics as you create your own quantum simulator in Python, then move on to using the QDK and the new Q# language for writing and running algorithms very different to those found in classical computing. Key Features· The underlying mechanics of how quantum computers work· How to simulate qubits in Python· Q# and the Microsoft Quantum Developer Kit· How to apply quantum algorithms to real-world examples For readers with basic programming skills and some experience of linear algebra, calculus and complex numbers. About the technology Quantum computing is the next step in computing power and scalability, with the potential to impact everything from data science to information security. Using qubits, the fundamental unit of quantum information, quantum computers can solve problems beyond the scale of classical computing. Software packages like Microsoft's Quantum Development Kit and the Q# language are now emerging to give programmers a quick path to exploring quantum development for the first time.

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

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

The Art of Network Penetration Testing is a guide to simulating an internal security breach. You’ll take on the role of the attacker and work through every stage of a professional pentest, from information gathering to seizing control of a system and owning the network.Summary Penetration testing is about more than just getting through a perimeter firewall. The biggest security threats are inside the network, where attackers can rampage through sensitive data by exploiting weak access controls and poorly patched software. Designed for up-and-coming security professionals, The Art of Network Penetration Testing teaches you how to take over an enterprise network from the inside. It lays out every stage of an internal security assessment step-by-step, showing you how to identify weaknesses before a malicious invader can do real damage. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Penetration testers uncover security gaps by attacking networks exactly like malicious intruders do. To become a world-class pentester, you need to master offensive security concepts, leverage a proven methodology, and practice, practice, practice. Th is book delivers insights from security expert Royce Davis, along with a virtual testing environment you can use to hone your skills. About the book The Art of Network Penetration Testing is a guide to simulating an internal security breach. You’ll take on the role of the attacker and work through every stage of a professional pentest, from information gathering to seizing control of a system and owning the network. As you brute force passwords, exploit unpatched services, and elevate network level privileges, you’ll learn where the weaknesses are—and how to take advantage of them. What's inside     Set up a virtual pentest lab     Exploit Windows and Linux network vulnerabilities     Establish persistent re-entry to compromised targets     Detail your findings in an engagement report About the reader For tech professionals. No security experience required. About the author Royce Davis has orchestrated hundreds of penetration tests, helping to secure many of the largest companies in the world. Table of Contents 1 Network Penetration Testing PHASE 1 - INFORMATION GATHERING 2 Discovering network hosts 3 Discovering network services 4 Discovering network vulnerabilities PHASE 2 - FOCUSED PENETRATION 5 Attacking vulnerable web services 6 Attacking vulnerable database services 7 Attacking unpatched services PHASE 3 - POST-EXPLOITATION AND PRIVILEGE ESCALATION 8 Windows post-exploitation 9 Linux or UNIX post-exploitation 10 Controlling the entire network PHASE 4 - DOCUMENTATION 11 Post-engagement cleanup 12 Writing a solid pentest deliverable

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

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.

Kafka in Action is a practical, hands-on guide to building Kafka-based data pipelines. Filled with real-world use cases and scenarios, this book probes Kafka's most common use cases, ranging from simple logging through managing streaming data systems for message routing, analytics, and more. In systems that handle big data, streaming data, or fast data, it's important to get your data pipelines right. Apache Kafka is a wicked-fast distributed streaming platform that operates as more than just a persistent log or a flexible message queue. Key Features· Understanding Kafka's concepts· Implementing Kafka as a message queue· Setting up and executing basic ETL tasks· Recording and consuming streaming data· Working with Kafka producers and consumers from Java applications· Using Kafka as part of a large data project team· Performing Kafka developer and admin tasks Written for intermediate Java developers or data engineers. No prior knowledge of Kafka is required. About the technology Apache Kafka is a distributed streaming platform for logging and streaming data between services or applications. With Kafka, it's easy to build applications that can act on or react to data streams as they flow through your system. Operational data monitoring, large scale message processing, website activity tracking, log aggregation, and more are all possible with Kafka. Dylan Scott is a software developer with over ten years of experience in Java and Perl. His experience includes implementing Kafka as a messaging system for a large data migration, and he uses Kafka in his work in the insurance industry.

Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you'll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Key Features· Introduction to computer vision· Deep learning and neural network· Transfer learning and advanced CNN architectures· Image classification and captioning For readers with intermediate Python, math and machine learningskills. About the technology By using deep neural networks, AI systems make decisions based on their perceptions of their input data. Deep learning-based computer vision (CV) techniques, which enhance and interpret visual perceptions, makes tasks like image recognition, generation, and classification possible. Mohamed Elgendy is the head of engineering at Synapse Technology, a leading AI company that builds proprietary computer vision applications to detect threats at security checkpoints worldwide. Previously, Mohamed was an engineering manager at Amazon, where he developed and taught the deep learning for computer vision course at Amazon's Machine Learning University. He also built and managed Amazon's computer vision think tank, among many other noteworthy machine learning accomplishments. Mohamed regularly speaks at many AI conferences like Amazon's DevCon, O'Reilly's AI conference and Google's I/O.

Turn the corner from ?Haskell student? to ?Haskell developer.? Haskell in Depth explores the important language features and programming skills you'll need to build production-quality software using Haskell. And along the way, you'll pick up some interesting insights into why Haskell looks and works the way it does. Get ready to go deep! Haskell in Depth is the perfect second book on Haskell. After a quick refresher on Haskell basics, this hands-on guide dives into examples and application scenarios designed to teach how Haskell works and how to apply it correctly. You'll learn about managing projects with Cabal and Stack, tackle error-handling and testing, and package programs and libraries for production deployment. Key Features· Organizing your projects with Cabal and Stack· Testing and profiling· Working with data· Building web services and networking apps· Using the sophisticated libraries like lens, vinyl, and servant Written for developers familiar with Haskell basics. About the technology As software becomes more complex, it's essential to program efficiently using tools and techniques that guarantee your applications will run correctly, grow easily, and last a long time. Haskell is a functional programming language that blends a mathematically-rigorous approach to software design with a tested ecosystem of tools and libraries you can use to build deployable applications.