Programmierung
JavaServer™ Faces und Jakarta Server Faces 2.3
DAS ARBEITSBUCH FÜR JAVA-WEBENTWICKLER // - Steigen Sie mit diesem fundierten Arbeitsbuch in die Entwicklung von Benutzerschnittstellen mit JavaServerTM Faces und Jakarta Server Faces 2.3 ein. - Anhand einer Beispielanwendung werden alle wichtigen Aspekte von JSF erläutert. - Vertiefen und erweitern Sie Ihre Fertigkeiten mit den zahlreichen Übungen. - Verwendet werden ausschließlich Open-Source-Systeme, so dass Sie alle Übungen und Beispiele ohne weitere Lizenzkosten nachvollziehen können. - Im Internet: Quell-Code zu den Beispielen und Lösungen der Übungen auf der Autorenwebsite zum Buch und GitHub - Ihr exklusiver Vorteil: E-Book inside beim Kauf des gedruckten Buches JavaServerTM Faces und Jakarta Server Faces 2.3 sind ein Framework für die Entwicklung von Benutzerschnittstellen für bzw. als Teil einer Java-Web-Anwendung. Dieses Arbeitsbuch führt Sie Schritt für Schritt in die Programmierung mit JSF ein. Sie erfahren, wie Sie damit moderne Benutzerschnittstellen für die Praxis entwickeln. Und natürlich geht es auch darum, wie JSF in eine Java-Web-Anwendung zu integrieren sind. Behandelt werden auch Themen wie die Anbindung an eine Datenbank mit JPA, die Verwendung von CDI sowie Authentifizierung und Autorisierung. Verfolgen Sie Schritt für Schritt die Entwicklung einer betrieblichen Anwendung und lernen Sie so anhand realer Aufgabenstellungen alle wichtigen Aspekte von JSF 2.3 kennen. Mit Hilfe der Übungen, deren Lösungen sich von der Website zum Buch und von GitHub herunterladen lassen, können Sie das Gelernte selbst ausprobieren und umsetzen. AUS DEM INHALT // Einleitung/JSF im Detail/Context und Dependency Injection/Weiterführende Themen/Classic Models/Spezialthemen/Verwendete Systeme/Ausblick/Anhang: Die Tags der Standardbibliotheken
C für Dummies (3. Auflg.)
Der Programmiersprachenklassiker C ist beliebt wie eh und je: denn mit C bringt man Computer zum Laufen. C steckt in der Software vieler Betriebssysteme. Dan Gookin bietet in diesem Buch eine wunderbar anschauliche und humorvolle Einführung.C erfreut sich als Klassiker unter den Programmiersprachen großer Beliebtheit, denn es steckt nach wie vor in vielen Betriebssystemen, Schnittstellen und Treibern, aber auch in Compilern und neueren eingebetteten Systemen. Wer C lernen und dabei auch noch Spaß haben möchte, sollte zu diesem Buch vom Urvater der Dummies, Dan Gookin, greifen. Mit viel Humor und vielen anschaulichen Beispielen macht er die Programmiersprache C lebendig.Dan Gookin schrieb das erste "... für Dummies"-Buch "DOS für Dummies" und hat seitdem unzählige Bücher für die Reihe verfasst. Er versteht es besonders gut, anschaulich und humorvoll zu erklären.
CRAN Recipes
Want to use the power of R sooner rather than later? Don’t have time to plow through wordy texts and online manuals? Use this book for quick, simple code to get your projects up and running. It includes code and examples applicable to many disciplines. Written in everyday language with a minimum of complexity, each chapter provides the building blocks you need to fit R’s astounding capabilities to your analytics, reporting, and visualization needs.CRAN Recipes recognizes how needless jargon and complexity get in your way. Busy professionals need simple examples and intuitive descriptions; side trips and meandering philosophical discussions are left for other books.Here R scripts are condensed, to the extent possible, to copy-paste-run format. Chapters and examples are structured to purpose rather than particular functions (e.g., “dirty data cleanup” rather than the R package name “janitor”). Everyday language eliminates the need to know functions/packages in advance.WHAT YOU WILL LEARN* Carry out input/output; visualizations; data munging; manipulations at the group level; and quick data exploration* Handle forecasting (multivariate, time series, logistic regression, Facebook’s Prophet, and others)* Use text analytics; sampling; financial analysis; and advanced pattern matching (regex)* Manipulate data using DPLYR: filter, sort, summarize, add new fields to datasets, and apply powerful IF functions* Create combinations or subsets of files using joins* Write efficient code using pipes to eliminate intermediate steps (MAGRITTR)* Work with string/character manipulation of all types (STRINGR)* Discover counts, patterns, and how to locate whole words* Do wild-card matching, extraction, and invert-match* Work with dates using LUBRIDATE* Fix dirty data; attractive formatting; bad habits to avoidWHO THIS BOOK IS FORProgrammers/data scientists with at least some prior exposure to R.WILLIAM A. YARBERRY, JR., CPA, CISA, is principal consultant, ICCM Consulting LLC, based in Houston, Texas. His practice is focused on IT governance, Sarbanes-Oxley compliance, security consulting, and business analytics for cost management. He was previously a senior manager with PricewaterhouseCoopers, responsible for telecom and network services in the Southwest region. Yarberry has more than 30 years’ experience in a variety of IT-related services, including application development, internal audit management, outsourcing administration, and Sarbanes-Oxley consulting.His books include The Effective CIO (co-authored), Computer Telephony Integration, $250K Consulting, DPLYR, 50,000 Random Numbers, Telecommunications Cost Management, and GDPR: A Short Primer. In addition, he has written over 20 professional articles on topics ranging from wireless security to change management. One of his articles, "Audit Rights in an Outsource Environment," received the Institute of Internal Auditors Outstanding Contributor Award.Prior to joining PricewaterhouseCoopers, Yarberry was director of telephony services for Enron Corporation. He was responsible for operations, planning, and architectural design for voice communications servers and related systems for more than 7,000 employees. Yarberry graduated Phi Beta Kappa in chemistry from the University of Tennessee and earned an MBA at the University of Memphis. He enjoys reading history, swimming, hiking, and spending time with family.1: DPLYR2: STRINGR3: Lubridate4: Regular Expressions: Introduction5: Typical Uses6: Some Simple Patterns7: Character Classes8: Elements of Regular Expressions9: The Magnificent Seven10: Regular Expressions in Stringr11: Unicode12: Tools for Development and Resources13: Regex Summary14: Recipes for Common R Tasks15: Data Structures16: Visualization17: Simple Prediction Methods18: Smorgasbord of Simple Statistical Tests19: Validation of Data20: Shortcuts and Miscellaneous21: ConclusionAppendices
Pointers in C Programming
Gain a better understanding of pointers, from the basics of how pointers function at the machine level, to using them for a variety of common and advanced scenarios. This short contemporary guide book on pointers in C programming provides a resource for professionals and advanced students needing in-depth hands-on coverage of pointer basics and advanced features. It includes the latest versions of the C language, C20, C17, and C14.You’ll see how pointers are used to provide vital C features, such as strings, arrays, higher-order functions and polymorphic data structures. Along the way, you’ll cover how pointers can optimize a program to run faster or use less memory than it would otherwise.There are plenty of code examples in the book to emulate and adapt to meet your specific needs.WHAT YOU WILL LEARN* Work effectively with pointers in your C programming* Learn how to effectively manage dynamic memory* Program with strings and arrays* Create recursive data structures* Implement function pointersWHO THIS BOOK IS FORIntermediate to advanced level professional programmers, software developers, and advanced students or researchers. Prior experience with C programming is expected.Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. He has a background in math and computer science, including experience programming and teaching in the C and R programming languages. For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.1. Pointers and the random access memory model2. Memory management3. Strings and arrays4. Recursive data structures5. Function pointers
JavaScript - Das Handbuch für die Praxis
Seit 25 Jahren das begleitende Grundlagenwerk zu JavaScript - Durchgehend überarbeiteter Bestseller in der 7. Auflage - Deckt die Version ES2020 inkl. Tools/Extensions & Node.js ab < - Vermittelt umfassendes und tiefgehendes JavaScript-Know-how JavaScript ist die Programmiersprache des Webs und der Bestseller "JavaScript: Das Handbuch für die Praxis" seit fast 25 Jahren und über sieben Auflagen ein geschätztes Grundlagenwerk für diese Sprache. Umfassend und detailliert dokumentiert Flanagan die wichtigsten client- und serverseitigen APIs. Die 7. Auflage wurde vollständig aktualisiert und deckt die Version 2020 von JavaScript ab. Freuen Sie sich auf spannende und inspirierende Codebeispiele und neue Kapitel über Klassen, Module, Iteratoren, Generatoren, Promises und async/await. Das Buch wendet sich an JavaScript-Neulinge mit Programmierkenntnissen sowie JavaScript-Programmierende, die ihr Verständnis vertiefen wollen. Die Zeit, die Sie in die Lektüre investieren, wird sich durch eine deutlich gesteigerte Produktivität garantiert rasch auszahlen.
Machine Learning - kurz & gut (2. Auflg.)
Der kompakte Schnelleinstieg in Machine Learning und Deep Learning in der neuen 2. Auflage 04/2021!Machine Learning beeinflusst heute beinahe alle Bereiche der Technik und der Gesellschaft. Dieses Buch bietet Interessierten, die einen technischen Hintergrund haben, die schnellstmögliche Einführung in das umfangreiche Themengebiet des maschinellen Lernens und der statistischen Datenanalyse. Dabei werden folgende Themen behandelt und mit praktischen Beispielen veranschaulicht:Datenimport und -vorbereitungSupervised LearningFeature-Auswahl, ModellvalidierungNeuronale Netze und Deep LearningUnsupervised LearningReinforcement LearningAnhand konkreter Datensätze lernen Sie einen typischen Workflow kennen: vom Datenimport über Datenbereinigung, Datenanalyse bis hin zur Datenvisualisierung. Die Codebeispiele basieren auf Python und den Bibliotheken Scikit-Learn, Pandas, NumPy, TensorFlow und Keras.Nach der Lektüre dieses Buchs haben Sie einen Überblick über das gesamte Thema und können Ansätze einordnen und bewerten. Das Buch vermittelt Ihnen eine solide Grundlage, um Ihre ersten eigenen Machine-Learning-Modelle zu trainieren und vertiefende Literatur zu verstehen.
JavaScript - Das Handbuch für die Praxis (7. Auflg.)
Seit 25 Jahren das begleitende Grundlagenwerk zu JavaScript, in 7. Auflage 04/2021JavaScript ist die Programmiersprache des Web und wird heute von mehr Softwareentwicklerinnen und -entwicklern eingesetzt als jede andere Sprache. Seit fast 25 Jahren dient ihnen dieser Bestseller als Praxishandbuch und zuverlässiger Begleiter. Die vorliegende siebte Auflage wurde vollständig aktualisiert und deckt die Version 2020 von JavaScript ab.Sie finden in diesem Buch spannende und aufschlussreiche Codebeispiele sowie neue und erweiterte Kapitel zu Klassen, Modulen, Iteratoren, Generatoren, Promises und async/await. Es richtet sich an Programmiererinnen und Programmierer, die JavaScript lernen möchten, und an alle in der Webentwicklung, die ein tieferes Verständnis für die Sprache entwickeln und sie noch besser beherrschen wollen.Unter anderem werden folgende Themen behandelt:Typen, Variablen, Operatoren, Anweisungen, Objekte und ArraysFunktionen, Klassen, Module, Iteratoren, Generatoren, Promises und async/awaitDie Standardbibliothek von JavaScript: Datenstrukturen, reguläre Ausdrücke, JSON, Internationalisierung und URLsDie Webplattform: Dokumente, Komponenten, Grafiken, Netzwerkoptionen, Speicher und ThreadsNode.js: Puffer, Dateien, Streams, Threads, Kindprozesse, Webclients und WebserverWerkzeuge und Spracherweiterungen für professionelle JavaScript-Entwickler
SAP SuccessFactors Talent: Volume 1
Take an in-depth look at SAP SuccessFactors talent modules with this complete guide to configuration, administration, and best practices. This two-volume series follows a logical progression of SAP SuccessFactors modules that should be configured to complete a comprehensive talent management solution. The authors walk you through fully functional simple implementations in the primary chapters for each module before diving into advanced topics in subsequent chapters.In volume 1, we start with a brief introduction. The next two chapters jump into the Talent Profile and Job Profile Builder. These chapters lay the structures and data that will be utilized across the remaining chapters which detail each module. The following eight chapters walk you through building, administering, and using a goal plan in the Goal Management module as well as performance forms in the Performance Management module. The book also expands on performance topics with the 360 form and continuous performance management in two additional chapters. We then dive into configuring the calibration tool and how to set up calibration sessions in the next two chapters before providing a brief conclusion.Within each topic, the book touches on the integration points with other modules as well as internationalization. The authors also provide recommendations and insights from real world experience. Having finished the book, you will have an understanding of what comprises a complete SAP SuccessFactors talent management solution and how to configure, administer, and use each module within it.You will:· Develop custom talent profile portlets· Integrate Job Profile Builder with SAP SuccessFactors talent modules· Set up security, group goals, and team goals in goals management with sample XML· Configure and launch performance forms including rating scales and route maps· Configure and administrate the calibration module and its best practicesSUSAN TRAYNOR is an SAP SuccessFactors Certified Professional with more than 21 years of progressive experience in SAP HCM and SuccessFactors implementations. You can follow her on LinkedIn.MICHAEL A. WELLENS, M.S. is a certified SAP SuccessFactors consultant with over 15 years of human resources information systems implementation experience. He has successfully launched a variety of core HR and talent management solutions across a variety of fortune 500 companies around the world. You can follow him on LinkedIn or on Twitter at @mike_wellens.VENKI KRISHNAMOORTHY is an SAP SuccessFactors consultant. Venki has over 15 years of experience as a functional lead, project manager, and program manager in HCM transformation projects. Venki has completed over 35 full lifecycle implementations of SuccessFactors projects across multiple modules. You can follow Venki on LinkedIn or on Twitter at @venki_sap.Chapter 1: An Introduction to SAP SuccessFactors Talent ModulesChapter 2: Talent ProfileChapter 3: Job Profile BuilderChapter 4: Basic Goal ManagementChapter 5: Alternate Goal Management Concepts and FunctionalityChapter 6: Introduction to Performance ManagementChapter 7: Performance Form Template SectionsChapter 8: Administering Performance Management FormsChapter 9: Using Performance Management FormsChapter 10: Performance Management XML and TranslationsChapter 11: Ask for Feedback, Get Feedback, Add Modifier, and Add SignerChapter 12: 360Chapter 13: Continuous Performance ManagementChapter 14: Calibration ConfigurationChapter 15: Calibration SessionsChapter 16: Conclusion
Beginning HCL Programming
Get started with programming and using the Hashicorp Language (HCL). This book introduces you to the HCL syntax and its ecosystem then it shows you how to integrate it as part of an overall DevOps approach.Next, you’ll learn how to implement infrastructure as code, specifically, using the Terraform template, a set of cloud infrastructure automation tools. As part of this discussion, you’ll cover Consul, a service mesh solution providing a full-featured control plane with service discovery, configuration, and segmentation functionality. You’ll integrate these with Vault to build HCL-based infrastructure as code solutions.Finally, you’ll use Jenkins and HCL to provision and maintain the infrastructure as code system. After reading and using Beginning HCL Programming, you'll have the know-how and source code to get started with flexible HCL for all your cloud and DevOps needs.WHAT YOU WILL LEARN* Get started with programming and using HCL* Use Vault, Consul, and Terraform * Apply HCL to infrastructure as codeDefine the Terraform template with HCL * Configure Consul using HCL* Use HCL to configure Vault* Provision and maintain infrastructure as code using Jenkins and HCLWHO THIS BOOK IS FORAnyone new to HCL but who does have at least some prior programming experience as well as knowledge of DevOps in general.PIERLUIGI RITI is a senior DevOps engineer at Coupa Software and Sunchronoss Technologies. Prior to that, he was a senior software engineer at Ericsson and Tata. His experience includes implementing DevOps in the cloud using Google Cloud Platform as well as AWS and Azure. Also, he has over ten years of extensive experience in more general design and development of different scale applications particularly in the telco and financial industries. He has quality development skills using the latest technologies including Java, J2EE, C#, F#, .NET, Spring .NET, EF, WPF, WF, WinForm, WebAPI, MVC, Nunit, Scala, Spring, JSP, EJB, Struts, Struts2, SOAP, REST, C, C++, Hibernate, NHibernate, Weblogic, XML, XSLT, Unix script, Ruby, and Python.DAVID FLYNN is an Associate Analyst in Employee Access Business Operations at Mastercard. He is an Electronic Engineer with experience in telecommunications, networks, software, security and Financial Systems. David started out as a Telecommunications Engineer working on Voice, data and wireless systems for Energis and later Nortel Networks supporting systems such as Lucent G3r, Alcatel E10 & Nortel Passport. He then did some time in Transport and Private security abroad before retraining in Computing, Cyber Security and Cloud Systems plus doing Cyber Security & Telecomm research for the Civil Service. He has completed separate Diplomas in Computing and Cloud focusing on Windows, C# , Google, AWS and Powershell amongst other technologies. David also has worked as a C# Engineer. More recently David has worked for various fintech companies including Bank Of America Merril Lynch focusing on technical & Application Support encompassing such technologies as Rsa Igl, Rsa SecurID, IBM Tam/Isam, Postgres/Oracle databases, Mainframe, Tandem, CyberArk, MaxPro and Active Directory.1 Introduction to HCLDefine the history of HCL, the basic syntax and, show the basic configuration syntax and the basic usage of the HCL2 The Hashicorp ecosystemShow the different software create by Hashicorpt like Vault, Consul, Terraform3 Introduction to GoA small introduction on the Go language, we use Go to define the configuration template described in the book4 Infrastructure As CodeDefine what is the Infrastructure as Code and how we can do that5 Introduction to the Cloud and DevOpsIn this chapter, we have a short introduction to the Cloud and the DevOps6 Use HCL for TerraformWe start to use the HCL for define Terraform template7 Consul HCLIn this chapter we introduce the HCL for Consul, we learn how to configure Consul using the HCL8 Vault HCLUse the HCL for configure Vault9 Infrastructure as Code with HCLDesign the Infrastructure as Code use the Hashicorp language, in particular, we use Terraform, Vault and Consul10 Provisioning and Maintain the Infrastructure as CodeIn this chapter, we see how to use Jenkins and the HCL for provisioning and maintain the infrastructure as code
Deep Learning with Python
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group.You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms.You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.WHAT YOU'LL LEARN* Review machine learning fundamentals such as overfitting, underfitting, and regularization.* Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.* Apply in-depth linear algebra with PyTorch* Explore PyTorch fundamentals and its building blocks* Work with tuning and optimizing models WHO THIS BOOK IS FORBeginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.Nikhil S. Ketkar currently leads the Machine Learning Platform team at Flipkart, India’s largest e-commerce company. He received his Ph.D. from Washington State University. Following that he conducted postdoctoral research at University of North Carolina at Charlotte, which was followed by a brief stint in high frequency trading at Transmaket in Chicago. More recently he led the data mining team in Guavus, a startup doing big data analytics in the telecom domain and Indix, a startup doing data science in the e-commerce domain. His research interests include machine learning and graph theory.Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over five years of industrial experience and is a published author of the book Smarter Decisions – The Intersection of IoT and Decision Science. He has worked with several industry leaders on high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist. He was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the world’s largest pure-play analytics provider and worked with the leaders of many Fortune 50 clients. He later worked with Flutura – an IoT analytics startup and GE. He currently resides in Vancouver, BC. Apart from writing books on decision science and IoT, Jojo has also been a technical reviewer for various books on machine learning, deep learning and business analytics with Apress and Packt publications. He is an active data science tutor and maintains a blog at http://blog.jojomoolayil.com.CHAPTER 1 – INTRODUCTION DEEP LEARNINGA brief introduction to Machine Learning and Deep Learning. We explore foundational topics within the subject that provide us the building blocks for several topics within the subject.CHAPTER 2 – INTRODUCTION TO PYTORCHA quick-start guide to PyTorch and a comprehensive introduction to tensors, linear algebra and mathematical operations for Tensors. The chapter provides the required PyTorch foundations for readers to meaningfully implement practical Deep Learning solutions for various topics within the book. Advanced PyTorch topics are explored as and when touch-based during the course of exercises in later chapter.CHAPTER 3- FEED FORWARD NETWORKS (30 PAGES)In this chapter, we explore the building blocks of a neural network and build an intuition on training and evaluating networks. We briefly explore loss functions, activation functions, optimizers, backpropagation, that could be used for training. Finally, we would stitch together each of these smaller components into a full-fledged feed-forward neural network with PyTorch.CHAPTER 4-AUTOMATIC DIFFERENTIATION IN DEEP LEARNINGIn this chapter we open this black box topic within backpropagation that enables training of neural networks i.e. automatic differentiation. We cover a brief history of other techniques that were ruled out in favor of automatic differentiation and study the topic with a practical example and implement the same using PyTorchs Autograd module.CHAPTER 5 – TRAINING DEEP NEURAL NETWORKSIn this chapter we explore few additional important topics around deep learning and implement them into a practical example. We will delve into specifics of model performance and study in detail about overfitting and underfitting, hyperparameter tuning and regularization. Finally, we will leverage a real dataset and combined our learnings from the beginning of this book into a practical example using PyTorch.CHAPTER 6 – CONVOLUTIONAL NEURAL NETWORKS (35 PAGES)Introduction to Convolutional Neural Networks for Computer Vision. We explore the core components with CNNs with examples to understand the internals of the network, build an intuition around the automated feature extraction, parameter sharing and thus understand the holistic process of training CNNs with incremental building blocks. We also leverage hands-on exercises to study the practical implementation of CNNs for a simple dataset i.e. MNIST (classification of handwritten digits), and later extend the exercise for a binary classification use-case with the popular cats and dogs’ dataset.CHAPTER 7 – RECURRENT NEURAL NETWORKSIntroduction to Recurrent Neural Networks and its variants (viz. Bidirectional RNNs and LSTMs). We explore the construction of a recurrent unit, study the mathematical background and build intuition around how RNNs are trained by exploring a simple four step unrolled network. We then explore hands-on exercises in natural language processing that leverages vanilla RNNs and later improve their performance by using Bidirectional RNNS combined with LSTM layers.CHAPTER 8 – RECENT ADVANCES IN DEEP LEARNINGA brief note of the cutting-edge advancements in the field will be added. We explore important inventions within the field with no implementation details, however focus on the applications and the path forward.
Practical Machine Learning for Streaming Data with Python
Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights.You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.WHAT YOU'LL LEARN* Understand machine learning with streaming data concepts* Review incremental and online learning* Develop models for detecting concept drift* Explore techniques for classification, regression, and ensemble learning in streaming data contexts* Apply best practices for debugging and validating machine learning models in streaming data context* Get introduced to other open-source frameworks for handling streaming data.WHO THIS BOOK IS FORMachine learning engineers and data science professionalsDr. Sayan Putatunda is an experienced data scientist and researcher. He holds a Ph.D. in Applied Statistics/ Machine Learning from the Indian Institute of Management, Ahmedabad (IIMA) where his research was on streaming data and its applications in the transportation industry. He has a rich experience of working in both senior individual contributor and managerial roles in the data science industry with multiple companies such as Amazon, VMware, Mu Sigma, and more. His research interests are in streaming data, deep learning, machine learning, spatial point processes, and directional statistics. As a researcher, he has multiple publications in top international peer-reviewed journals with reputed publishers. He has presented his work at various reputed international machine learning and statistics conferences. He is also a member of IEEE.Chapter 1: An Introduction to Streaming DataChapter Goal: Introduce the readers to the concept of streaming data, the various challenges associated with it, some of its real-world business applications, various windowing techniques along with the concepts of incremental and online learning algorithms. This chapter will also help in understanding the concept of model evaluation in case of streaming data and provide and introduction to the Scikit-Multiflow framework in Python.No of pages- 35Sub -Topics1. Streaming data2. Challenges of streaming data3. Concept drift4. Applications of streaming data5. Windowing techniques6. Incremental learning and online learning7. Illustration : Adopting batch learners into incremental learners8. Introduction to Scikit-Multiflow framework9. Evaluation of streaming algorithmsChapter 2: Change DetectionChapter Goal: Help the readers to understand the various change detection/concept drift detection algorithms and its implementation on various datasets using Scikit-Multiflow.No of pages : 35Sub - Topics:1. Change detection problem2. Concept drift detection algorithms3. ADWIN4. DDM5. EDDM6. Page HinkleyChapter 3: Supervised and Unsupervised Learning for Streaming DataChapter Goal: Help the readers to understand the various regression and classification (including Ensemble Learning) algorithms for streaming data and its implementation on various datasets using Scikit-Multiflow. Also, discuss some approaches for clustering with streaming data and its implementation using Python.No of pages: 35Sub - Topics:1. Regression with streaming data2. Classification with streaming data3. Ensemble Learning with streaming data4. Clustering with streaming dataChapter 4: Other Tools and the Path ForwardChapter Goal: Introduce the readers to the other open source tools for handling streaming data such as Spark streaming, MOA and more. Also, educate the reader about additional reading for advanced topics within streaming data analysis.No of pages: 35Sub - Topics:1. Other tools for handling streaming data1.1.1. Apache Spark1.1.2. Massive Online Analysis (MOA)1.1.3. Apache Kafka2. Active research areas and breakthroughs in streaming data analysis3. Conclusion
C Programming on Raspberry Pi
The Raspberry Pi has traditionally been programmed using Python. Although this is a very powerful language, many programmers may not be familiar with it. C on the other hand is perhaps the most commonly used programming language and all embedded microcontrollers can be programmed using it.The C language is taught in most technical colleges and universities and almost all engineering students are familiar with using it with their projects. This book is about using the Raspberry Pi with C to develop a range of hardware-based projects. Two of the most popular C libraries, wiringPi and pigpio are used.The book starts with an introduction to C and most students and newcomers will find this chapter invaluable. Many projects are provided in the book, including using Wi-Fi and Bluetooth to establish communication with smartphones.Many sensor and hardware-based projects are included. Both wiringPi and pigpio libraries are used in all projects. Complete program listings are given with full explanations. All projects have been fully tested and work.The following hardware-based projects are provided in the book:> Using sensors> Using LCDs> I 2 C and SPI buses> Serial communication> Multitasking> External and timer interrupts> Using Wi-Fi> Webservers> Communicating with smartphones> Using Bluetooth> Sending data to the cloudProgram listings of all Raspberry Pi projects developed in this book are available on the Elektor website. Readers can download and use these programs in their projects. Alternatively, they can customize them to suit their applications.Prof. Dr. Dogan Ibrahim is a Fellow of the Institution of Electrical Engineers. He is the author of over 60 technical books, published by publishers including Wiley, Butterworth, and Newnes. He is the author of over 250 technical papers, published in journals, and presented in seminars and conferences
Der pragmatische Programmierer (2. Auflg.)
Ihr Weg zur Meisterschaft - die 2. vollständig überarbeitete Auflage, 04/2021.Der Pragmatische Programmierer ist eines dieser seltenen Fachbücher, die Sie im Lauf der Jahre immer wieder lesen werden. Egal, ob Sie Programmiereinsteiger oder erfahrener Praktiker sind, stets können Sie neue Einsichten gewinnen.David Thomas und Andrew Hunt schrieben 1999 die erste Ausgabe dieses einflussreichen Buches, um ihren Kunden zu helfen, bessere Software zu entwickeln und die Freude am Programmieren wiederzuentdecken. Ihre pragmatischen Profitipps helfen bis heute einer ganzen Generation von Programmierern, den Kosmos der Software-Entwicklung zu erkunden, unabhängig von einer bestimmten Sprache oder Methodik oder einem bestimmten Framework.Ihre »Pragmatische Philosophie« hat den Weg bereitet für Hunderte von Büchern, Screencasts und Hörbüchern sowie Tausende von Karrieren und Erfolgsgeschichten. Zwanzig Jahre später untersuchen die Autoren nun erneut, was einen modernen Programmierer ausmacht. Die Themen reichen von persönlicher Verantwortung über berufliche Entwicklung bis hin zu Architekturtechniken, die Ihren Code flexibel, leicht anpassbar und wiederverwendbar halten.In kurzen Abschnitten, die auch einzeln gelesen werden können, erläutern die Autoren nicht nur die Best Practices, sondern auch Fallstricke bei der Software-Entwicklung. Anschauliche Beispiele und interessante Analogien machen dieses Buch zu einem Lesevergnügen.Egal ob Einsteiger, erfahrener Programmierer oder Manager für Softwareprojekte: Wer die Profitipps der Autoren bei der täglichen Arbeit anwendet, wird seine Produktivität, Genauigkeit und Zufriedenheit rasch steigern und damit als Pragmatischer Programmierer auch langfristig erfolgreich seinErfahren Sie im Buch, wie Sie:den Verfall von Software bekämpfenRedundanz vermeidenflexiblen, dynamischen und anpassungsfähigen Quelltext schreibenIhr Handwerkszeug optimal nutzenvermeiden, mit dem Zufall zu programmierendie richtigen Anforderungen findensich vor Sicherheitslücken schützenProbleme beim nebenläufigen Code lösenTeams aus Pragmatischen Programmierern bildeneffektiv testendas Pragmatic Starter Kit implementierenIhre Anwender begeisternLeseprobe (PDF-Link)
Neuronale Netze mit C# programmieren
Mit praktischen Beispielen für Machine Learning im Unternehmenseinsatz.Sie wollen neuronale Netze und Machine-Learning-Algorithmen mit C# entwickeln? Dann finden Sie in diesem Buch eine gut verständliche Einführung in die Grundlagen und es wird Ihnen gezeigt, wie Sie neuronale Netze und Machine-Learning-Algorithmen in Ihren eigenen Projekten praktisch einsetzen.Mithilfe von Beispielen erstellen und trainieren Sie Ihr erstes neuronales Netz zur vorausschauenden Wartung einer Produktionsmaschine.Im Praxisteil lernen Sie dann, wie Sie TensorFlow-Modelle in ML.NET benutzen oder Infer.NET direkt verwenden können. Des Weiteren nutzen Sie die Predictive- und Sentiment-Analyse, um sich mit Machine-Learning-Algorithmen vertraut zu machen.Alle im Buch vorgestellten Projekte sind in C# programmiert und stehen als Download zur Verfügung. Grundkenntnisse in C# werden für die Arbeit mit dem Buch vorausgesetzt. Alle Projekte lassen sich ohne größere Rechnerressourcen umsetzen.Daniel Basler arbeitet als Lead Developer und Softwarearchitekt. Seine Schwerpunkte liegen auf Cross-Platform-Apps, Android, JavaScript und Microsoft-Technologien. Er entwickelt u.a. Software für Regal- und Flächenlagersysteme sowie Anlagenvisualisierung und setzt in diesem Umfeld verstärkt Machine-Learning-Methoden ein. Darüber hinaus schreibt er regelmäßig Artikel für die Fachzeitschriften dotnetpro und web&mobile Developer.Leseprobe (PDF-Link)
PHP 8 Objects, Patterns, and Practice
Learn how to develop elegant and rock-solid systems using PHP, aided by three key elements: object fundamentals, design principles, and best practices. The 6th edition of this popular book has been fully updated for PHP 8, including attributes, constructor property promotion, new argument and return pseudo-types, and more. It also covers many features new since the last edition including typed properties, the null coalescing operator, and void return types. This book provides a solid grounding in PHP's support for objects, it builds on this foundation to instill core principles of software design and then covers the tools and practices needed to develop, test, and deploy robust code.PHP 8 Objects, Patterns, and Practice begins by covering PHP's object-oriented features. It introduces key topics including class declarations, inheritance, and reflection. The next section is devoted to design patterns. It explains the principles that make patterns powerful. You’ll cover many of the classic design patterns including enterprise and database patterns. The last segment of the book covers the tools and practices that can help turn great code into a successful project. The section shows how to manage multiple developers and releases with git, and how to manage builds and dependencies with Composer. It also explores strategies for automated testing and continuous integration.After reading and using this book, you will have mastered object-oriented enhancements, design patterns, and the essential development tools available for PHP 8.WHAT YOU WILL LEARN* Work with object fundamentals: write classes and methods, instantiate objects, and create powerful class hierarchies using inheritanceMaster advanced object-oriented features, including static methods and properties, managing error conditions with exceptions, and creating abstract classes and interfaces * Understand and use design principles to deploy objects and classes effectively in your projects* Discover a set of powerful patterns that you can implement in your own projects* Guarantee a successful project including unit testing; version control and build, installation, and package management; and continuous integrationWHO THIS BOOK IS FORAnyone with at least a basic knowledge of PHP who wants to use its object-oriented features in their projects. It is also for PHP coders who want to learn about the practices and tools (version control, testing, continuous integration, etc) that can make projects safe, elegant and stable.MATT ZANDSTRA has worked as a web programmer, consultant, and writer for over two decades. He is the author of SAMS Teach Yourself PHP in 24 Hours (three editions) and is a contributor to DHTML Unleashed. He has written articles for Linux Magazine, Zend, IBM DeveloperWorks, and php|architect Magazine, among others. Matt was a senior developer/tech lead at Yahoo and API tech lead at LoveCrafts. Matt works as a consultant advising companies on their architectures and system management, and also develops systems primarily with PHP, and Java. Matt also writes fiction.Part I. Objects.-1. PHP: Design and Management.-2. PHP and Objects.-3. Object Basics.-4. Advanced Features.-5. Object Tools.-6. Objects and Design.-Part II. Patterns.-7. What Are Design Patterns? Why Use Them?.-8. Some Pattern Principles.-9. Generating Objects.-10. Patterns for Flexible Object Programming.-11. Performing and Representing Tasks.-12. Enterprise Patterns.-13. Database Patterns.-Part III. Practice.-14. Good (and Bad) Practice.-15. PHP Standards.-16. PHP Using and Creating Components with Composer.-17. Version Control with Git.-18. Testing.-19. Automated Build with Phing.-20. Vagrant.-21. Continuous Integration.-22. Objects, Patterns, and Practice.-23. App A: Bibliography.-24. App B: A Simple Parser.
Visualizing Data in R 4
Master the syntax for working with R’s plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This book will take advantage of the new features added to R 4 where appropriate including a refreshed color palette for charts, Cairo graphics with more fonts/symbols, and improved performance from grid graphics including ggplot 2 rendering speed.Visualizing Data in R 4 starts with an introduction and then is split into two parts and six appendices. Part I covers the function plot() and the ancillary functions you can use with plot(). You’ll also see the functions par() and layout(), providing for multiple plots on a page. Part II goes over the basics of using the functions qplot() and ggplot() in the package ggplot2. The default plots generated by the functions qplot() and ggplot() give more sophisticated-looking plots than the default plots done by plot() and are easier to use, but the function plot() is more flexible. Both plot() and ggplot() allow for many layers to a plot.The six appendices will cover plots for contingency tables, plots for continuous variables, plots for data with a limited number of values, functions that generate multiple plots, plots for time series analysis, and some miscellaneous plots. Some of the functions that will be in the appendices include functions that generate histograms, bar charts, pie charts, box plots, and heatmaps.WHAT YOU WILL LEARN* Use R to create informative graphics* Master plot(), qplot(), and ggplot()* Discover the canned graphics functions in stats and graphicsFormat plots generated by plot() and ggplot()WHO THIS BOOK IS FORThose in data science who use R. Some prior experience with R or data science is recommended.Margot Tollefson, PhD is a semi-retired freelance statistician, with her own consulting business, Vanward Statistics. She received her PhD in statistics from Iowa State University and has many years of experience applying R to statistical research problems. Dr. Tollefson has chosen to write this book because she often creates graphics using R and would like to share her knowledge and experience. Her professional blog is on WordPress at vanwardstat. Social media: @vanstat1) Introduction: plot(), qplot(), and ggplot(), Plus Somea) plot() – arguments, ancillary functions, and methods; par() and layout()b) qplot() and ggplot() – aesthetics, geometries, and other useful functionsc) other plotting functions in graphics and statsPart I. An Overview of plot()2) The plot() Functiona) what the function is and how the function worksb) will use method .xy for example3) The Arguments to plot()a) Type of plot, axis labels, plot titles, display formatb) Plotting characters, character size, fonts, colors, line styles and widths4) Ancillary Functions to use with plot()a) axis(), box(), clip(), grid(), legend(), mtext(), rug()b) abline(), contour(), curve(), lines(), polypath()c) arrows(), image(), points(), polygon(), rect(), segments(), symbols(), text()d) axTicks(), identify(), locator(), pch(), strwidth(),5) The Methods for plot()a) What are methods?b) Methods in the graphics packagec) Methods in the stats package6) How to Use the Functions par() and layout()a) What par() doesb) Arguments specific to par()c) Multiple plotsPart II. A look at the ggplot2 Package7) The Functions qplot(), ggplot(), and the Specialized Notation in ggplot2a) Working with qplot()b) The ggplot() functionc) Specialized notation8) Themesa) The theme() functionb) The element_*() functions9) Aesthetics and Geometriesa) The aes() functionb) The geom_*() functions10) Controlling the Appearancea) The annotate_*() functionsb) The coord_*() functionsc) The facet_*() functionsd) The guide_*() functionse) The position_*() functionsf) The scale_*() functionsg) The stat_*() functionsAppendix I. Plots for Contingency TablesAppendix II. Plots for Continuous VariablesAppendix III. Plots for Data with a Limited Number of ValuesAppendix IV. Functions that Generate Multiple PlotsAppendix V. Plots for Time SeriesAppendix VI. Miscellaneous Plots
R2DBC Revealed
Understand the newest trend in database programming for developers working in Java, Kotlin, Clojure, and other JVM-based languages. This book introduces Reactive Relational Database Connectivity (R2DBC), a modern way of connecting to and querying relational databases from Java and other JVM languages. The book begins by helping you understand not only what reactive programming is, but why it is necessary. Then building on those fundamentals, the book takes you into the world of databases and the newly released Reactive Relational Database Connectivity (R2DBC) specification.Examples in the book are worked using the freely available MariaDB database along with MariaDB’s vendor-implementation of the R2DBC service-provider interface (SPI). Following along with the examples and the provided example code helps prepare you to work with any of the growing number of R2DBC implementations for popular enterprise databases such as Oracle Database and SQL Server. You’ll be well prepared for what is becoming the future of database access from Java and other languages built on the JVM.WHAT YOU WILL LEARN* Understand why R2DBC was created and how it utilizes the Reactive Streams API * Understand the components of the R2DBC service-provider interface* Create and manage reactive database connections and connection pools using an R2DBC client* Programmatically execute queries on a relational database using an R2DBC client* Effectively utilize transactions using an R2DBC client* Build relational database-driven applications that are event-driven and non-blockingWHO THIS BOOK IS FORSoftware developers building solutions using JVM languages and the JVM ecosystem, and developers who need an introduction to the R2DBC specification and reactive programming with relational databases and want to understand what Reactive Relational Database Connectivity is and why it came about. This book includes practical examples of using the R2DBC specification with Java and MariaDB that will provide developers with the knowledge they need to create their own solutions.ROB HEDGPETH is a professional software engineer and developer relations enthusiast residing in the bustling metropolis of Chicago, Illinois. Rob has more than 12 years of professional development experience, primarily in the application development space. Throughout the years he has contributed to the architecture and development of many apps, using a large array of languages and technologies. Now as a developer advocate and evangelist for MariaDB, Rob gets to combine his love for technology with his mission to fuel developers' curiosity and passion. IntroductionPART I. THE REACTIVE MOVEMENT AND R2DBC1. The Case for Reactive Programming2. Introduction to R2DBCPART II. THE R2DBC SERVICE-PROVIDER INTERFACE3. The Path to Implementation4. Connections5. Transactions6. Statements7. Handling Results8. Result Metadata9. Mapping Data Types10. Handling ExceptionsPART III. GETTING STARTED WITH R2DBC AND MARIADB11. Getting Stated with R2DBC12. Managing Connections13. Managing Data14. Managing Transactions15. Connection Pooling16. Practical Applications with Spring Data and R2DBC
Objektorientierte Programmierung (5. Auflg.)
Unverzichtbare Skills für guten Code - das umfassende Handbuch in 5. Auflage.Komplexe Systeme solide strukturieren, tragfähige Designs erstellen, robusten Code schreiben und wartbare Software liefern: Dafür brauchen Sie ein Repertoire, aus dem die Objektorientierung heute nicht mehr wegzudenken ist. Dieses umfassende und praxisnahe Lehrbuch hilft Ihnen, die Prinzipien der Objektorientierung zu verstehen und zur Basis Ihrer Arbeit zu machen.Leseprobe (PDF-Link)
Deep Reinforcement Learning with Python
Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise.You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods.You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym.WHAT YOU'LL LEARN* Examine deep reinforcement learning * Implement deep learning algorithms using OpenAI’s Gym environment* Code your own game playing agents for Atari using actor-critic algorithms* Apply best practices for model building and algorithm training WHO THIS BOOK IS FORMachine learning developers and architects who want to stay ahead of the curve in the field of AI and deep learning.Nimish is a passionate technical leader who brings to table extreme focus on use of technology for solving customer problems. He has over 25 years of work experience in the Software and Consulting. Nimish has held leadership roles with P&L responsibilities at PwC, IBM and Oracle. In 2006 he set out on his entrepreneurial journey in Software consulting at SOAIS with offices in Boston, Chicago and Bangalore. Today the firm provides Automation and Digital Transformation services to Fortune 100 companies helping them make the transition from on-premise applications to the cloud.He is also an angel investor in the space of AI and Automation driven startups. He has co-founded Paybooks, a SaaS HR and Payroll platform for Indian market. He has also cofounded a Boston based startup which offers ZipperAgent and ZipperHQ, a suite of AI driven workflow and video marketing automation platforms. He currently hold the position as CTO and Chief Data Scientist for both these platforms.Nimish has an MBA from Indian Institute of Management in Ahmedabad, India and a BS in Electrical Engineering from Indian Institute of Technology in Kanpur, India. He also holds multiple certifications in AI and Deep Learning.Chapter 1: Introduction to Deep Reinforcement LearningChapter Goal: Introduce the reader to field of reinforcement learning and setting the context of what they will learn in rest of the bookSub -Topics1. Deep reinforcement learning2. Examples and case studies3. Types of algorithms with mind-map4. Libraries and environment setup5. SummaryChapter 2: Markov Decision ProcessesChapter Goal: Help the reader understand models, foundations on which all algorithms are built.Sub - Topics1. Agent and environment2. Rewards3. Markov reward and decision processes4. Policies and value functions5. Bellman equationsChapter 3: Model Based AlgorithmsChapter Goal: Introduce reader to dynamic programming and related algorithmsSub - Topics:1. Introduction to OpenAI Gym environment2. Policy evaluation/prediction3. Policy iteration and improvement4. Generalised policy iteration5. Value iterationChapter 4: Model Free ApproachesChapter Goal: Introduce Reader to model free methods which form the basis for majority of current solutionsSub - Topics:1. Prediction and control with Monte Carlo methods2. Exploration vs exploitation3. TD learning methods4. TD control5. On policy learning using SARSA6. Off policy learning using q-learningChapter 5: Function ApproximationChapter Goal: Help readers understand value function approximation and Deep Learning use in Reinforcement Learning.1. Limitations to tabular methods studied so far2. Value function approximation3. Linear methods and features used4. Non linear function approximation using deep LearningChapter 6: Deep Q-LearningChapter Goal: Help readers understand core use of deep learning in reinforcement learning. Deep q learning and many of its variants are introduced here with in depth code exercises.1. Deep q-networks (DQN)2. Issues in Naive DQN3. Introduce experience replay and target networks4. Double q-learning (DDQN)5. Duelling DQN6. Categorical 51-atom DQN (C51)7. Quantile regression DQN (QR-DQN)8. Hindsight experience replay (HER)Chapter 7: Policy Gradient AlgorithmsChapter Goal: Introduce reader to concept of policy gradients and related theory. Gain in depth knowledge of common policy gradient methods through hands-on exercises1. Policy gradient approach and its advantages2. The policy gradient theorem3. REINFORCE algorithm4. REINFORCE with baseline5. Actor-critic methods6. Advantage actor critic (A2C/A3C)7. Proximal policy optimization (PPO)8. Trust region policy optimization (TRPO)Chapter 8: Combining Policy Gradients and Q-LearningChapter Goal: Introduce reader to the trade offs between two approaches ways to connect together the two seemingly dissimilar approaches. Gain in depth knowledge of some land mark approaches.1. Tradeoff between policy gradients and q-learning2. The connection3. Deep deterministic policy gradient (DDPG)4. Twin delayed DDPG (TD3)5. Soft actor critic (SAC)Chapter 9: Integrated Learning and PlanningChapter Goal: Introduce reader to the scalable approaches which are sample efficient for scalable problems.1. Model based reinforcement learning2. Dyna and its variants3. Guided policy search4. Monte Carlo tree search (MCTS)5. AlphaGoChapter 10: Further Exploration and Next StepsChapter Goal: With the backdrop of having gone through most of the popular algorithms, readers are now introduced again to exploration vs exploitation dilemma, central to reinforcement learning.1. Multi arm bandits2. Upper confidence bound3. Thompson sampling
Practical C++20 Financial Programming
Apply C++ to programming problems in the financial industry using this hands-on book, updated for C++20. It explains those aspects of the language that are more frequently used in writing financial software, including the Standard Template Library (STL), templates, and various numerical libraries. Practical C++20 Financial Programming also describes many of the important problems in financial engineering that are part of the day-to-day work of financial programmers in large investment banks and hedge funds. The author has extensive experience in the New York City financial industry that is now distilled into this handy guide.Focus is on providing working solutions for common programming problems. Examples are plentiful and provide value in the form of ready-to-use solutions that you can immediately apply in your day-to-day work. You’ll see examples of matrix manipulations, curve fitting, histogram generation, numerical integration, and differential equation analysis, and you’ll learn how all these techniques can be applied to some of the most common areas of financial software development.These areas include performance price forecasting, optimizing investment portfolios, and more. The book style is quick and to-the-point, delivering a refreshing view of what one needs to master in order to thrive as a C++ programmer in the financial industry.WHAT YOU WILL LEARN* Cover aspects of C++ especially relevant to financial programmingWrite working solutions to commonly encountered problems in finance * Design efficient, numerical classes for use in finance, as well as to use those classes provided by Boost and other librariesWHO THIS BOOK IS FORThose who are new to programming for financial applications using C++, but should have some previous experience with C++.CARLOS OLIVEIRA works in the area of quantitative finance, with more than ten years of experience in creating scientific and financial models in C++. During his career, Carlos has developed several large-scale applications for financial companies such as Bloomberg L.P. and F-Squared Investments. Carlos Oliveira obtained a PhD in operations research and systems engineering from the University of Florida, an MSc in computer science from UFC (Brazil), and a BSc in computer science from UECE (Brazil). He has also performed academic research in the field of combinatorial optimization, with applications in diverse areas such as finance, telecommunications, computational biology, and logistics. Carlos has written more than 30 academic papers on optimization, and authored three books, including Options and Derivatives Programming in C++20 (Apress, 2020).1. The Fixed-Income Market2. The Equities Market3. C++ Programming Techniques in Finance4. Common Libraries for Financial Code5. Designing Numerical Classes6. Plotting Financial Data7. Linear Algebra8. Interpolation9. Calculating Roots of Equations10. Numerical Integration11. Solving Partial Differential Equations12. Algorithm Optimization13. Portfolio Optimization14. Monte Carlo Methods for Equity markets15. Extending Financial Libraries16. C++ with R and Octave17. MultithreadingA. Appendix A: C++20 Features
Grundkurs Theoretische Informatik
Theoretische Informatik – der Vorlesungsbegleiter. Berechenbarkeit, formale Sprachen, Algorithmik und Komplexitätstheorie sind theoretische Themen mit praktischer Relevanz, zu denen es ebenso praktische Zugänge gibt. Freuen Sie sich auf eine moderene Didaktik, die streng Formales mit Ihrer Intuition verknüpft, lernfreundlich ausarbeitet und schließlich zu jedem Thema Anwendungsfelder der Informatik vorstellt. Stefan Neubert hat nicht nur selbst Freude an der theoretischen Informatik, sondern widmet sich auch mit Leidenschaft ihrer Vermittlung zu Beginn und im Laufe des Bachelorstudiums. Eine Einführung mit vielen Aufgaben und Beispielen, auch zum Selbststudium geeignet. Aus dem Inhalt: Grundlegende mathematische NotationModelle und Grenzen der BerechenbarkeitFormale Sprachen: Endliche Automaten, kontextfreie Grammatiken, Pumping Lemmata und mehrBeweisverfahren für Korrektheit und Laufzeit von AlgorithmenParadigmen für den AlgorithmenentwurfAmortisierte Analyse und untere Schranke für LaufzeitenNP-Vollständigkeit und Reduktion 1. Einführung ... 15 1.1 ... Kompetenzen für die theoretische Arbeit ... 16 1.2 ... Themen der theoretischen Informatik ... 18 1.3 ... Anleitung fürs Buch ... 20 1.4 ... Danksagungen ... 21 2. Mathematische Notation ... 23 2.1 ... Logische Aussagen ... 24 2.2 ... Mengen ... 27 2.3 ... Relationen und Funktionen ... 32 2.4 ... Graphen ... 37 2.5 ... Unendlichkeiten und Abzählbarkeit ... 40 2.6 ... Beweistechniken ... 42 2.7 ... Aufgaben ... 57 2.8 ... Lösungen ... 58 TEIL I. Berechenbarkeit und formale Sprachen ... 65 3. Einführung in die Berechenbarkeitstheorie ... 67 3.1 ... Algorithmus ... 68 3.2 ... Zu viele Funktionen ... 69 3.3 ... Das Halteproblem ... 70 3.4 ... Kontrollfragen ... 72 3.5 ... Antworten ... 72 4. Problemtypen ... 73 4.1 ... Formalisierung von Problemen ... 73 4.2 ... Funktionen berechnen ... 75 4.3 ... Datencodierung ... 75 4.4 ... Sprachen entscheiden ... 78 4.5 ... Problemklassen der Berechenbarkeitstheorie ... 79 4.6 ... Aufgaben ... 82 4.7 ... Lösungen ... 83 5. Einführung in formale Sprachen ... 85 5.1 ... Definition ... 85 5.2 ... Die Chomsky-Hierarchie ... 88 5.3 ... Aufgaben ... 89 5.4 ... Lösungen ... 90 6. Reguläre Sprachen ... 91 6.1 ... Deterministische endliche Automaten ... 92 6.2 ... Nichtdeterministische endliche Automaten ... 103 6.3 ... Grammatiken ... 111 6.4 ... Reguläre Ausdrücke ... 120 6.5 ... Abschlusseigenschaften ... 127 6.6 ... Entscheidungsprobleme auf regulären Sprachen ... 132 6.7 ... Äquivalenzklassenzerlegung ... 134 6.8 ... Nichtreguläre Sprachen ... 139 6.9 ... Ausblick ... 144 6.10 ... Aufgaben ... 144 6.11 ... Lösungen ... 149 7. Kontextfreie Sprachen ... 161 7.1 ... Kontextfreie Grammatiken ... 162 7.2 ... Eindeutige Ableitungsbäume ... 164 7.3 ... Chomsky-Normalform ... 166 7.4 ... Exkurs: Kellerautomaten ... 170 7.5 ... Abschlusseigenschaften ... 175 7.6 ... Entscheidungsprobleme auf kontextfreien Sprachen ... 176 7.7 ... Nicht-kontextfreie Sprachen ... 181 7.8 ... Ausblick ... 183 7.9 ... Aufgaben ... 184 7.10 ... Lösungen ... 186 8. Kontextsensitive Sprachen ... 193 8.1 ... Kontextsensitive und monotone Grammatiken ... 194 8.2 ... Das Wortproblem auf kontextsensitiven Sprachen ... 195 9. Aufzählbare Sprachen ... 197 9.1 ... Turingmaschinen ... 199 9.2 ... While-Programme ... 202 9.3 ... Gödelnummern ... 218 9.4 ... Das universelle While-Programm ... 220 9.5 ... Das schrittbeschränkte universelle While-Programm ... 223 9.6 ... Diagonalisierung und min-Suche ... 224 9.7 ... Prädikate für semi-entscheidbare Sprachen ... 226 9.8 ... Semi-Entscheidbarkeit vs. Aufzählbarkeit ... 227 9.9 ... Das S-m-n-Theorem ... 228 9.10 ... Das kleenesche Rekursionstheorem ... 230 9.11 ... Weitere Modelle und Charakterisierungen ... 233 9.12 ... Aufgaben ... 233 9.13 ... Lösungen ... 235 10. Nicht Berechenbares ... 241 10.1 ... Beweise mit KRT ... 243 10.2 ... Der Satz von Rice ... 244 10.3 ... Reduktionen ... 246 10.4 ... RE-Vollständigkeit ... 250 10.5 ... Ausblick: Die arithmetische Hierarchie ... 251 10.6 ... Aufgaben ... 252 10.7 ... Lösungen ... 254 TEIL II. Algorithmik ... 259 11. Einführung in Algorithmik ... 261 12. Obere Schranken für Laufzeiten ... 263 12.1 ... Das Maschinenmodell ... 264 12.2 ... Die Laufzeit eines Algorithmus ... 267 12.3 ... Die Größe einer Eingabe ... 268 12.4 ... Die Landau-Notation ... 268 12.5 ... Aufgaben ... 271 12.6 ... Lösungen ... 272 13. Laufzeiten von Datenstrukturen ... 275 13.1 ... Arrays ... 275 13.2 ... Listen ... 277 13.3 ... Verschachtelte Datenstrukturen und Graphen ... 279 13.4 ... Aufgaben ... 281 13.5 ... Lösungen ... 282 14. Brute-Force-Algorithmen ... 285 14.1 ... Lineare Suche ... 286 14.2 ... Backtracking/Tiefensuche ... 288 14.3 ... Aufgaben ... 292 14.4 ... Lösungen ... 293 15. Greedy-Algorithmen ... 295 15.1 ... Beweis mit Austauschargument ... 296 15.2 ... Greedy stays ahead ... 302 15.3 ... Aufgaben ... 304 15.4 ... Lösungen ... 306 16. Divide and Conquer ... 313 16.1 ... Mergesort ... 314 16.2 ... Binäre Suche ... 319 16.3 ... Multiplikation großer Zahlen ... 321 16.4 ... Das Mastertheorem ... 325 16.5 ... Ausblick ... 326 16.6 ... Aufgaben ... 327 16.7 ... Lösungen ... 329 17. Dynamische Programmierung ... 335 17.1 ... Fibonacci-Zahlen ... 336 17.2 ... Rückgeld geben ... 337 17.3 ... Der Algorithmus von Dijkstra ... 341 17.4 ... Aufgaben ... 344 17.5 ... Lösungen ... 346 18. Amortisierte Analyse ... 351 18.1 ... Dynamische Arrays ... 351 18.2 ... Guthabenmethode ... 353 18.3 ... Ausblick ... 353 TEIL III. Komplexitätstheorie ... 355 19. Einführung in die Komplexitätstheorie ... 357 19.1 ... Die Komplexität eines Problems ... 358 19.2 ... Bedingte Schranken ... 358 19.3 ... Auswege für schwierige Probleme ... 359 20. Beweistechniken für untere Schranken ... 361 20.1 ... Die Ausgabegröße ... 362 20.2 ... Das informationstheoretische Argument ... 363 20.3 ... Das Adversary-Argument ... 367 20.4 ... Reduktionen ... 370 20.5 ... Aufgaben ... 372 20.6 ... Lösungen ... 374 21. P vs. NP: Bedingte untere Schranken ... 377 21.1 ... Die Komplexitätsklasse P ... 378 21.2 ... Die Komplexitätsklasse NP ... 380 21.3 ... Polynomialzeitreduktionen ... 388 21.4 ... NP-schwere und NP-vollständige Probleme ... 392 21.5 ... Ausblick: Mehr NP-vollständige Probleme ... 404 21.6 ... Aufgaben ... 405 21.7 ... Lösungen ... 406 22. Ausblick: Parametrisierte Analyse ... 408 Index ... 410
Python 3 Schnelleinstieg
* PROGRAMMIEREN LERNEN OHNE VORKENNTNISSE* IN 14 KAPITELN SCHRITT FÜR SCHRITT ZUM PROFI* EINFACHE PRAXISBEISPIELE UND ÜBUNGSAUFGABENMit diesem Buch gelingt Ihnen der Einstieg in die Python-Programmierung ohne Mühe. Sie benötigen keinerlei Vorkenntnisse.Alle Grundlagen werden anschaulich und einfach nachvollziehbar anhand von Codebeispielen erklärt. Übungsaufgaben in unterschiedlichen Schwierigkeitsstufen am Ende der Kapitel helfen Ihnen, das neu gewonnene Wissen praktisch anzuwenden und zu vertiefen.Der Autor führt Sie Schritt für Schritt in die Welt der Programmierung ein: von den Grundlagen über Objektorientierung bis zur Entwicklung von Anwendungen mit grafischer Benutzungsoberfläche. Dabei lernen Sie ebenfalls, was guten Programmierstil ausmacht und wie man Fehler in Programmtexten finden und von vornherein vermeiden kann.So gelingt es Ihnen in Kürze, Python effektiv in der Praxis einzusetzen.* Alle Grundlagen einfach erläutert* Verarbeitung von Texten und Bildern* Objektorientierte Programmierung* Grafische Benutzungsoberflächen mit tkinter* Testen, Debugging und Performance-Analyse* Übungsaufgaben in drei verschiedenen Schwierigkeitsstufen* Programmcode, Lösungen und Glossar zum DownloadMichael Weigend war mehr als 30 Jahre lang als Lehrer tätig und hält an der Universität Münster Vorlesungen zur Python-Programmierung. Er hat bereits mehrere Bücher zu den Themen Programmierung, Web Development und visuelle Modellierung geschrieben.
Python One-Liners
Python in seiner raffiniertesten FormPython Tipps und Tricks mit fortgeschrittenem NiveauProblemlösungen für den ProgrammieralltagNumPy-GrundlagenPython One-Liners zeigt Ihnen, wie man "One Liners", Einzeiler, liest und schreibt: prägnante Ausdrücke zu nützlicher Funktionalität, verpackt in einer einzigen Codezeile. Sie lernen systematisch jede Zeile von Python-Code zu entschlüsseln und zu verstehen, und werden wie ein Experte eloquentes, stark komprimiertes Python schreiben.In den fünf Kapiteln des Buches werden Tipps und Tricks, reguläre Ausdrücke, Machine Learning, Kernthemen der Datenwissenschaft und hilfreiche Algorithmen behandelt. Die ausführlichen Erläuterungen von Einzeilern führen in wichtige Konzepte der Informatik ein und fördern Ihre Programmier- und Analysefähigkeiten.Sie lernen fortgeschrittene Python-Funktionen wie Listenverständnis, Slicing, Lambda-Funktionen, reguläre Ausdrücke, Map- und Reduce-Funktionen und Slice-Zuweisungen kennen.Sie erwerben darüber hinaus Kenntnisse in diesen Bereichen:- Nutzung von Datenstrukturen zur Lösung von Problemen aus der realen Welt, wie z. B. die Verwendung boolescher Indizierung zum Auffinden von Städten mit überdurchschnittlicher Umweltverschmutzung.- Verwendung der NumPy-Grundlagen wie Array, Form, Achse, Typ, Broadcasting, fortgeschrittene Indizierung, Slicing, Sortierung, Suche, Aggregation und Statistik.- Berechnen Sie grundlegende Statistiken von mehrdimensionalen Datenfeldern und die K-Means-Algorithmen für unsupervised Learning.- Erstellen Sie fortgeschrittenere reguläre Ausdrücke unter Verwendung von Gruppierungs- und benannten Gruppen, negativen Lookaheads, maskierten Zeichen, Leerzeichen, Zeichensätzen (und negativen Zeichensätzen) und greedy/non greedy Operatoren.- Ein breites Spektrum von Informatik-Themen verstehen, einschließlich Anagramme, Palindrome, Obermengen, Permutationen, Fakultäten, Primzahlen, Fibonacci-Zahlen, Obfuszierung, Suche und algorithmische Sortierung.Am Ende des Buches werden Sie wissen, wie man Python in seiner raffiniertesten Form schreibt und prägnante, schöne Python-Kunstwerke in nur einer einzigen Zeile schafft.Christian Mayer hat einen Doktortitel in Informatik und ist der Gründer der beliebten Python-Site Finxter (https:blog.finxter.com). Mayer ist außerdem der Autor der Coffee Break Python-Reihe.
Praktische Statistik für Data Scientists
Statistische Konzepte aus der Perspektive von Data Scientists erläutert* Das Buch stellt die Verbindung zwischen nützlichen statistischen Prinzipien und der heutigen Datenanalyse-Praxis her* Ermöglicht Data Scientists, ihr Wissen über Statistik auf ein neues Level zu bringen* Übersetzung der 2. Auflage des US-Bestsellers mit Beispielen in Python und RStatistische Methoden sind ein zentraler Bestandteil der Arbeit mit Daten, doch nur wenige Data Scientists haben eine formale statistische Ausbildung. In Kursen und Büchern über die Grundlagen der Statistik wird das Thema aber selten aus der Sicht von Data Scientists behandelt. Viele stellen daher fest, dass ihnen eine tiefere statistische Perspektive auf ihre Daten fehlt.Dieses praxisorientierte Handbuch mit zahlreichen Beispielen in Python und R erklärt Ihnen, wie Sie verschiedene statistische Methoden speziell in den Datenwissenschaften anwenden. Es zeigt Ihnen auch, wie Sie den falschen Gebrauch von statistischen Methoden vermeiden können, und gibt Ratschläge, welche statistischen Konzepte für die Datenwissenschaften besonders relevant sind. Wenn Sie mit R oder Python vertraut sind, ermöglicht diese zugängliche, gut lesbare Referenz es Ihnen, Ihr statistisches Wissen für die Praxis deutlich auszubauen.Peter Bruce ist Gründer des Institute for Statistics Education bei Statistics.com. Andrew Bruce ist Principal Research Scientist bei Amazon und verfügt über mehr als 30 Jahre Erfahrung in Statistik und Data Science. Peter Gedeck ist Senior Data Scientist bei Collaborative Drug Discovery, er entwickelt Machine-Learning-Algorithmen für die Vorhersage von Eigenschaften von Arzneimittelkandidaten.