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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.

Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems.

The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage. Azure Data Engineering teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system.

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.

We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

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.

Unit Testing: Principles, Patterns and Practices shows you how to refine your existing unit tests by implementing modern best practices. YouGÇÖll learn to spot which tests are performing, which need refactoring, and which need to be deleted entirely! Upgrade your testing suite with new testing styles, good patterns, and reliable automated testing. Unit Testing: Principles, Practices and Patterns is a practical guide to modern unit testing best practices. Microsoft MVP Vladimir Khorikov takes you hands-on with examples of the ideal unit test and unit testing practices, building your skills step by step on a solid foundation. Key Features -+ A universal frame of reference by which to assess any unit test -+ Common anti-patterns to identify and avoid -+ Guidelines on how to refactor a test suite along with the -+ production code it covers -+ Using integration tests to verify the behavior of the system as a whole For readers who know the basics of unit testing and want to improve their techniques to get the best return on their efforts. Examples are in C# but apply in any language About the technology Test automation has turned unit testing into a standard practice in software development. The right unit test suite gives great outcomes and is vital to delivering quality software. Author Bio Vladimir Khorikov is an author, blogger, and Microsoft MVP. He has been developing software professionally for over ten years, and has mentored numerous teams on the ins and outs of unit testing.

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you'll learn to take advantage of Spark's core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists, Spark in Action, Second Edition is designed for data engineers and software engineers who want to master data processing using Spark without having to learn a complex new ecosystem of languages and tools. You'll instead learn to apply your existing Java and SQL skills to take on practical, real-world challenges. Key Features· Lots of examples based in the Spark Java APIs using real-life dataset and scenarios · Examples based on Spark v2.3 Ingestion through files, databases, and streaming · Building custom ingestion process · Querying distributed datasets with Spark SQL For beginning to intermediate developers and data engineers comfortable programming in Java. No experience with functional programming, Scala, Spark, Hadoop, or big data is required. About the technology Spark is a powerful general-purpose analytics engine that can handle massive amounts of data distributed across clusters with thousands of servers. Optimized to run in memory, this impressive framework can process data up to 100x faster than most Hadoop-based systems. Author BioAn experienced consultant and entrepreneur passionate about all things data, Jean-Georges Perrin was the first IBM Champion in France, an honor he's now held for ten consecutive years. Jean-Georges has managed many teams of software and data engineers.

Object Design Style Guide captures dozens of techniques for creating pro-quality OO code that can stand the test of time. Examples are in an instantly-familiar pseudocode, teaching techniques you can apply to any OO language, from C++ to PHP. The design rules for different types of objectsBest practices for naming objectsTesting an object's behavior instead of its implementationExercises for each chapter to test your design skills

With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the tools library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level!Key features• An introduction to functional and parallel programming • Data science workflow • Profiling code for better performance • Fulfilling different quality objectives for a single unifying task • Python multiprocessing • Practical exercises including full-scale distributed applicationsAudienceReaders should have intermediate Python programming skills.About the technologyPython is a data scientist's dream-come-true, thanks to readily available libraries that support tasks like data analysis, machine learning, visualization, and numerical computing.

Practices of the Python Pro teaches you to design and write software that's easy to understand, maintain, and scale. Using practical Python-based examples, experienced developer Dane Hillard introduces design concepts that lay the foundation for designing, building, and maintaining software that's good for the long haul. You'll learn established guidelines for clean coding style and explore techniques to address design considerations like speed, integrity, security, and extensibility. Organizing large Python projectsLeveraging abstraction for clean codeWriting flexible, reusable codeInheritance and compositionConsiderations for testing and performance

Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, (NLP) expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key featuresAn overview of NLP and deep learning• Models for textual similarity• Deep memory-based NLP • Semantic role labeling • Sequential NLPAudienceFor those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required.About the technologyNatural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learningStephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He's the technical coordinator of two large European Union-funded research security-related projects. He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

Type-related failures are common and can be very costly. Famously, in 1999, NASA's Mars Climate Orbiter burned up in the atmosphere because of an error that could have easily been prevented with typing. By taking advantage of the strong type systems available in most modern programming languages, you can eliminate whole classes of errors. Programming with Types teaches you type system techniques for writing software that's safe, correct, easy to test and maintain, and that practically documents itself. Master these techniques, and you may even help prevent an interstellar catastrophe!Key features• Designing functions that don't return values • An introduction to type systems and their benefits • Encoding text• Building data structures with arrays and references • Type composition • Function types • Type safety • Object-oriented programming with typesAudienceReaders need basic high school math skills as well as some programming experience in a mainstream programming language like Java, JavaScript, C#, or C++.About the technologyAs software grows in complexity, it's essential to adopt coding tools and strategies that enforce safety and correctness. A type system governs what types of data are allowable, requiring your code to accurately store, represent, and use its internal data.

Companies everywhere are moving everyday business processes over to the cloud, and AI is increasingly being given the reins in these tasks. As this massive digital transformation continues, the combination of serverless computing and AI promises to become the de facto standard for business-to-consumer platform development?and developers who can design, develop, implement, and maintain these systems will be in high demand! AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you'll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Key features Cloud AI from development to production Applying cloud AI services to your existing platform Understanding orchestration patterns for cloud AI systems How to architect and build scalable, resilient data pipelines AudienceFor software developers with intermediate skills in at least one programming language and a basic understanding of IP networking and HTTP protocol.About the technologyArtificial Intelligence (AI), a machine's ability to learn and make predictions based on patterns it identifies, is already being leveraged by businesses around the world in areas like targeted product recommendations, financial forecasting and resource planning, customer service chatbots, healthcare diagnostics, data security, and more.Peter Elger is the founder and CEO of fourTheorem, a software solutions company providing expertise on architecture, development, DevOps, and machine learning. He is also the cofounder and CTO of two other companies: nearForm, a Node.js consultancy, and StitcherAds, a digital advertising platform. He is a regular conference speaker and coauthor of The Node Cookbook as well as several academic papers. Eoin Shanaghy is the cofounder and CTO fourTheorem and the founder of Showpiper, a machine learning-driven video content marketing startup. He previously created real-time trading applications for Fortis Bank and developed Hewlett-Packard's enterprise application server as well as 3G network management systems for Ericsson and Arantech (now Netscout). Johannes Ahlmann is the founder and CEO of Sensatus.io, a Machine Learning company specialized in large-scale Web Crawling, Market Intelligence and On-Premise AI solutions. He previously created Information Extraction solutions as Head of Data Science at ScrapingHub, and created responsive mobile solutions at Dell EMC.

Thanks to the authors' easy-to-digest style, you'll effortlessly learn about types, object-oriented programming with classes and interfaces, and using TypeScript with JavaScript libraries. You'll discover TypeScript's excellent tooling as you explore code-quality improvement with TSLint, debugging with source maps, unit testing, and more. TypeScript is JavaScript with an important upgrade! By adding a strong type system to JavaScript, TypeScript can help you eliminate entire categories of runtime errors. In TypeScript Quickly, you'll learn to build rock-solid apps through practical examples and hands-on projects under the expert instruction of experienced web developers Yakov Fain and Anton Moiseev. WILL SELL LIKE Angular Development with Typescript, Key features• Mastering TypeScript syntax • Object-oriented programming with classes and interfaces • Using TypeScript with JavaScript libraries • Multiple real-world code samplesAudienceWritten for intermediate web developers comfortable with JavaScript ES5 and HTML.About the technologyTypeScript is an extension of JavaScript that includes key language features such as optional static typing, compile-time error catching, and auto-complete. By specifying types and type annotations, your code becomes much easier to interpret, which improves productivity and team development. In particular, TypeScript makes complex applications like SPAs much easier to maintain and extend.Yakov Fain and Anton Moiseev are experienced web application developers. They authored two editions of Manning's Angular Development with TypeScript among other technical books. Yakov is a Java champion and a prolific tech blogger at yakov.fain.com.

Whether for building interactive browser-based applications or creating server-side applications in Node, JavaScript is the most widely used language for web programming. With new features, language improvements, paradigms, and potential use cases appearing regularly, there's never been a more exciting time to be a JavaScript developer. In The Joy of JavaScript, author and JavaScript expert Luis Atencio teaches you key design concepts that lead to clean, lean, modular, and easy-to-maintain code.Key features• JavaScript's objects and module system • Working with higher order functions • Driving application business logic with functional programming principles • Dynamically hooking into data with Proxy and Reflect APIs • Static type-checking with Flow • Taming complex asynchronous behavior using reactive programmingAudiencePerfect for intermediate JavaScript developers with basic familiarity with HTTP, HTML/CSS, and Git/CLI.About the technologyJavaScript is multi-paradigm, supporting object-oriented, functional, reactive, and event-driven styles of programming. And transpilers like Babel make it possible to compile code written in other languages into JavaScript.

?Atwood's Law? is driven by the idea that all applications will ultimately wind up on the web and therefore must be written in JavaScript. WebAssembly may be your way out! With WebAssembly, you can write in nearly any modern language and run your code in the browser through a memory-safe, sandboxed execution environment that can be embedded in a web browser and other platforms. Getting set up and moving with WebAssembly requires you to modify your web dev process; WebAssembly in Action will get you started quickly and guide you through real-world examples and detailed diagrams that help you create, run, and debug WebAssembly modules.Key features• The WebAssembly format • Dynamic linking of multiple modules at runtime • Communicating between modules and JavaScript • Debugging modules • Unreleased features like host bindings and garbage collectionAudienceWritten for developers with a basic understanding of C/C++, JavaScript, and HTML.About the technologyWebAssembly compiles code written in C, C++, Rust, and other highlevel languages into an ultra-compact bytecode files that run in the browser and other environments at near-native speeds. Besides vastly improved performance, opportunities to reuse existing code, and the freedom to build in your favorite language, this game-changer allows access to newer and faster libraries. WebAssembly is the joint effort of industry leaders like Microsoft, Mozilla, Google, and others, with

Humans learn best from feedback?we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you'll need to implement it into your own projects.Key features• Structuring problems as Markov Decision Processes • Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them • Applying reinforcement learning algorithms to real-world problemsAudienceYou'll need intermediate Python skills and a basic understanding of deep learning.About the technologyDeep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that's not all it can do!

If you think "Modern" and "C" don''t belong in the same sentence, think again. The C standards committee actively reviews and extends the language, with updated published C standards as recently as 2018. In Modern C, author Jens Gustedt teaches you the skills and features you need to write relevant programs in this tried-and-true language, including Linux and Windows, device drivers, web servers and browsers, smartphones, and much more! Modern C teaches you to take your C programming skills to new heights, whether you''re just starting out with C or have more extensive experience. Organized by level, this comprehensive guide lets you jump in where it suits you best while still reaping the maximum benefits. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Summary Classic Computer Science Problems in Python deepens your knowledge of problem-solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Computer science problems that seem new or unique are often rooted in classic algorithms, coding techniques, and engineering principles. And classic approaches are still the best way to solve them! Understanding these techniques in Python expands your potential for success in web development, data munging, machine learning, and more. About the Book Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. You'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You'll especially enjoy the feeling of satisfaction as you crack problems that connect computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview! What's Inside Search algorithms Common techniques for graphs Neural networks Genetic algorithms Adversarial search Uses type hints throughout Covers Python 3.7   About the Reader For intermediate Python programmers. About the Author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014) and Classic Computer Science Problems in Swift (Manning, 2018). Table of Contents Small problems Search problems Constraint-satisfaction problems Graph problems Genetic algorithms K-means clustering Fairly simple neural networks Adversarial search Miscellaneous problems    

SummaryDask is a native parallel analytics tool designed to integrate seamlessly with the libraries you''re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!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 TechnologyAn efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.About the BookData Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you''ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you''ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What''s insideWorking with large, structured and unstructured datasetsVisualization with Seaborn and DatashaderImplementing your own algorithmsBuilding distributed apps with Dask DistributedPackaging and deploying Dask appsAbout the ReaderFor data scientists and developers with experience using Python and the PyData stack.About the AuthorJesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.Table of ContentsPART 1 - The Building Blocks of scalable computingWhy scalable computing matters Introducing Dask PART 2 - Working with Structured Data using Dask DataFrames Introducing Dask DataFrames Loading data into DataFrames Cleaning and transforming DataFrames Summarizing and analyzing DataFrames Visualizing DataFrames with Seaborn Visualizing location data with Datashader PART 3 - Extending and deploying DaskWorking with Bags and Arrays Machine learning with Dask-ML Scaling and deploying Dask

SummaryCloud Native Patternsis your guide to developing strong applications that thrive in the dynamic, distributed, virtual world of the cloud. This book presents a mental model for cloud-native applications, along with the patterns, practices, and tooling that set them apart.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyCloud platforms promise the holy grail: near-zero downtime, infinite scalability, short feedback cycles, fault-tolerance, and cost control. But how do you get there? By applying cloudnative designs, developers can build resilient, easily adaptable, web-scale distributed applications that handle massive user traffic and data loads. Learn these fundamental patterns and practices, and you''ll be ready to thrive in the dynamic, distributed, virtual world of the cloud.About the BookWith 25 years of experience under her belt, Cornelia Davis teaches you the practices and patterns that set cloud-native applications apart. With realistic examples and expert advice for working with apps, data, services, routing, and more, she shows you how to design and build software that functions beautifully on modern cloud platforms. As you read, you will start to appreciate that cloud-native computing is more about the how and why rather than the where. What''s insideThe lifecycle of cloud-native appsCloud-scale configuration managementZero downtime upgrades, versioned services, and parallel deploysService discovery and dynamic routingManaging interactions between services, including retries and circuit breakersAbout the ReaderRequires basic software design skills and an ability to read Java or a similar language.About the AuthorCornelia Davis is Vice President of Technology at Pivotal Software. A teacher at heart, she''s spent the last 25 years making good software and great software developers.Table of ContentsPART 1 - THE CLOUD-NATIVE CONTEXTYou keep using that word: Defining "cloud-native"Running cloud-native applications in productionThe platform for cloud-native softwarePART 2 - CLOUD-NATIVE PATTERNSEvent-driven microservices: It''s not just request/responseApp redundancy: Scale-out and statelessnessApplication configuration: Not just environment variablesThe application lifecycle: Accounting for constant changeAccessing apps: Services, routing, and service discoveryInteraction redundancy: Retries and other control loopsFronting services: Circuit breakers and API gatewaysTroubleshooting: Finding the needle in the haystackCloud-native data: Breaking the data monolith