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R 763.00
Mastering Blockchain Programming with Solidity
Discover the advanced features of Solidity that will help you write high-quality code and develop secure smart contracts with the latest ERC standardsKey FeaturesDelve into Solidity and understand control structures, function calls, and variable scopesExplore tools for developing, testing, and debugging your blockchain applicationsLearn advanced design patterns and best practices for writing secure smart contractsBook DescriptionSolidity is among the most popular and contract-oriented programming languages used for writing decentralized applications (DApps) on Ethereum blockchain. If you’re looking to perfect your skills in writing professional-grade smart contracts using Solidity, this book can help.You will get started with a detailed introduction to blockchain, smart contracts, and Ethereum, while also gaining useful insights into the Solidity programming language. A dedicated section will then take you through the different Ethereum Request for Comments (ERC) standards, including ERC-20, ERC-223, and ERC-721, and demonstrate how you can choose among these standards while writing smart contracts. As you approach later chapters, you will cover the different smart contracts available for use in libraries such as OpenZeppelin. You’ll also learn to use different open source tools to test, review and improve the quality of your code and make it production-ready. Toward the end of this book, you’ll get to grips with techniques such as adding security to smart contracts, and gain insights into various security considerations. By the end of this book, you will have the skills you need to write secure, production-ready smart contracts in Solidity from scratch for decentralized applications on Ethereum blockchain.What you will learnTest and debug smart contracts with Truffle, Ganache, Remix, and MetaMaskGain insights into maintaining code quality with different toolsGet up to speed with ERC standards such as ERC-20 and ERC-721Become adept at using design patterns while writing smart contractsUse MultiSignature (MultiSig) wallets and improve the security of contractsUse Oracle services to fetch information from outside the blockchainWho this book is forThis book is for developers and data scientists who want to learn Ethereum, blockchain, and Solidity to write smart contracts and develop production-ready code. Basic knowledge of Solidity is assumed.
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Programming Machine Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.
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official Raspberry Pi Camera Module guide, 2nd Edition
Add the power of HDR photography, Full HD video, and AI image recognition to your Raspberry Pi projects with Camera Modules.Give intelligence and depth to your creations, making your projects smarter and more responsive. Plus, the new AI Kit supercharges your Raspberry Pi Camera Module with image recognition, enabling it to act and react to the world around it.This book is your step-by-step guide to adding a Camera Module to your Raspberry Pi board. You'll master still images, capture video clips as digital files, and control the camera with shell commands and Python code.Discover the numerous modes and effects available, and use the camera in a variety of exciting projects across 18 packed chapters:Getting started.Capturing photos and videos.Control the camera with precision.Add artificial intelligence with the Al Kit.Time-lapse photography.Selfies and stop-motion video.Build a bird box camera.Live-stream video and stills....and much more!
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Computer Science From Scratch
Computer science can feel unapproachable for those without a formal CS education. Fun Computer Science Projects in Python pulls back that curtain, illuminating several foundational CS concepts through creative, hands-on projects. Each of the 7 projects is presented in a code-centric tutorial that gently introduces topics like interpreters, emulators, and machine learning without getting bogged down by complex theory. The projects showcase advanced Python language features and clean code principles while exploring interesting algorithms. Chapters conclude with discussions of real-world applications of the topic and proposed exercises to extend the reader's skills. Covers Python 3.x
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Distributed Programming
Distributed Programming: Theory and Practice presents a practical and rigorous method to develop distributed programs that correctly implement their specifications. The method also covers how to write specifications and how to use them. Numerous examples such as bounded buffers, distributed locks, message-passing services, and distributed termination detection illustrate the method. Larger examples include data transfer protocols, distributed shared memory, and TCP network sockets. Distributed Programming: Theory and Practice bridges the gap between books that focus on specific concurrent programming languages and books that focus on distributed algorithms. Programs are written in a "real-life" programming notation, along the lines of Java and Python with explicit instantiation of threads and programs. Students and programmers will see these as programs and not "merely" algorithms in pseudo-code. The programs implement interesting algorithms and solve problems that are large enough to serve as projects in programming classes and software engineering classes. Exercises and examples are included at the end of each chapter with on-line access to the solutions. Distributed Programming: Theory and Practice is designed as an advanced-level text book for students in computer science and electrical engineering. Programmers, software engineers and researchers working in this field will also find this book useful.
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Mastering Delphi Programming: A Complete Reference Guide
Use structural, behavioral, and concurrent patterns in Delphi to skillfully develop applicationsKey FeaturesDelve into the core patterns and components of Delphi to enhance your application's designLearn how to select the right patterns to improve your program's efficiency and productivityDiscover how parallel programming and memory management can optimize your codeBook DescriptionDelphi is a cross-platform Integrated Development Environment (IDE) that supports rapid application development for most operating systems, including Microsoft Windows, iOS, and now Linux with RAD Studio 10.2. If you know how to use the features of Delphi, you can easily create scalable applications in no time.This Learning Path begins by explaining how to find performance bottlenecks and apply the correct algorithm to fix them. You'll brush up on tricks, techniques, and best practices to solve common design and architectural challenges. Then, you'll see how to leverage external libraries to write better-performing programs. You'll also learn about the eight most important patterns that'll enable you to develop and improve the interface between items and harmonize shared memories within threads. As you progress, you'll also delve into improving the performance of your code and mastering cross-platform RTL improvements.By the end of this Learning Path, you'll be able to address common design problems and feel confident while building scalable projects.This Learning Path includes content from the following Packt products:Delphi High Performance by Primož GabrijelčičHands-On Design Patterns with Delphi by Primož GabrijelčičWhat you will learnUnderstand parallel programming and work with the various tools included with DelphiExplore memory managers and their implementationLeverage external libraries to write better-performing programsKeep up to date with the latest additions and design techniques in DelphiGet to grips with various modern multithreading approachesBreak a design problem down into its component partsWho this book is forThis Learning Path is for intermediate-level Delphi programmers who want to build robust applications using Delphi features. Prior knowledge of Delphi is assumed.
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Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation
The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremor, and machine learning. The machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This achievement demonstrates preliminary insight with respect to the concept of Network Centric Therapy, which essentially represents the Internet of Things for healthcare and the biomedical industry, inclusive of wearable and wireless inertial sensor systems, machine learning, and access to Cloud computing resources.Imperative to the realization of these objectives is the organization of the software development process. Requirements and pseudo code are derived, and software automation using Python for post-processing the inertial sensor signal data to a feature set for machine learning is progressively developed. A perspective of machine learning in terms of a conceptual basis and operational overview is provided. Subsequently, an assortment of machine learning algorithms is evaluated based on quantification of a reach and grasp task for essential tremor using a smartphone as a wearable and wireless accelerometer system.Furthermore, these skills regarding the software development process and machine learning applications with wearable and wireless inertial sensor systems enable new and novel biomedical research only bounded by the reader's creativity.Related Link(s)
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Hands-On Parallel Programming with C# 8 and .NET Core 3
Enhance your enterprise application development skills by mastering parallel programming techniques in .NET and C#Key FeaturesWrite efficient, fine-grained, and scalable parallel code with C# and .NET CoreExperience how parallel programming works by building a powerful applicationLearn the fundamentals of multithreading by working with IIS and KestrelBook DescriptionIn today’s world, every CPU has a multi-core processor. However, unless your application has implemented parallel programming, it will fail to utilize the hardware’s full processing capacity. This book will show you how to write modern software on the optimized and high-performing .NET Core 3 framework using C# 8.Hands-On Parallel Programming with C# 8 and .NET Core 3 covers how to build multithreaded, concurrent, and optimized applications that harness the power of multi-core processors. Once you’ve understood the fundamentals of threading and concurrency, you’ll gain insights into the data structure in .NET Core that supports parallelism. The book will then help you perform asynchronous programming in C# and diagnose and debug parallel code effectively. You’ll also get to grips with the new Kestrel server and understand the difference between the IIS and Kestrel operating models. Finally, you’ll learn best practices such as test-driven development, and run unit tests on your parallel code.By the end of the book, you’ll have developed a deep understanding of the core concepts of concurrency and asynchrony to create responsive applications that are not CPU-intensive.What you will learnAnalyze and break down a problem statement for parallelismExplore the APM and EAP patterns and how to move legacy code to TaskApply reduction techniques to get aggregated resultsCreate PLINQ queries and study the factors that impact their performanceSolve concurrency problems caused by producer-consumer race conditionsDiscover the synchronization primitives available in .NET CoreUnderstand how the threading model works with IIS and KestrelFind out how you can make the most of server resourcesWho this book is forIf you want to learn how task parallelism is used to build robust and scalable enterprise architecture, this book is for you. Whether you are a beginner to parallelism in C# or an experienced architect, you’ll find this book useful to gain insights into the different threading models supported in .NET Standard and .NET Core. Prior knowledge of C# is required to understand the concepts covered in this book.
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Programming Microsoft Dynamics 365 Business Central
Explore the fundamentals of Dynamics 365 Business Central and the Visual Studio Code development environment with the help of useful examples and case studiesKey FeaturesTailor your applications to best suit the needs of your businessExplore the latest features of Business Central with examples curated by industry expertsIntegrate Business Central features in your applications with this comprehensive guideBook DescriptionMicrosoft Dynamics 365 Business Central is a full ERP business solution suite with a robust set of development tools to support customization and enhancement. These tools can be used to tailor Business Central's in-built applications to support complete management functions for finance, supply chain, manufacturing, and operations.Using a case study approach, this book will introduce you to Dynamics 365 Business Central and Visual Studio Code development tools to help you become a productive Business Central developer. You'll also learn how to evaluate a product's development capabilities and manage Business Central-based development and implementation. You'll explore application structure, the construction of and uses for each object type, and how it all fits together to build apps that meet special business requirements.By the end of this book, you'll understand how to design and develop high-quality software using the Visual Studio Code development environment, the AL language paired with the improved editor, patterns, and features.What you will learnProgramming using the AL language in the Visual Studio Code development environmentExplore functional design and development using ALHow to build interactive pages and learn how to extract data for usersHow to use best practices to design and develop modifications for new functionality integrated with the standard Business Central softwareBecome familiar with deploying the broad range of components available in a Business Central systemCreate robust, viable systems to address specific business requirementsWho this book is forIf you want to learn about Dynamics 365 Business Central's powerful and extensive built-in development capabilities, this is the book for you. ERP consultants and managers of Business Central development will also find this book helpful. Although you aren't expected to have worked with Dynamics Business Central, basic understanding of programming and familiarity with business application software will help you understand the concepts covered in this book.
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Programming Language Implementation and Logic Programming
This volume consists of the papers accepted for presentation at the second international workshop on Programming Language Implementation and Logic Programming (PLILP '90) held in Linköping, Sweden, August 20-22, 1990. The aim of the workshop was to identify concepts and techniques used both in implementation of programming languages, regardless of the underlying programming paradigm, and in logic programming. The intention was to bring together researchers working in these fields. The volume includes 26 selected papers falling into two categories. Papers in the first category present certain ideas from the point of view of a particular class of programming languages, or even a particular language. The ideas presented seem to be applicable in other classes of languages. Papers in the second category directly address the problem of integration of various programming paradigms. The proceedings of the predecessor workshop PLILP '88, held in Orléans, France, May 16-18, 1988, are available as Lecture Notes in Computer Science, Vol. 348.
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Electromagnetic Simulation Using the FDTD Method with Python
Provides an introduction to the Finite Difference Time Domain method and shows how Python code can be used to implement various simulations This book allows engineering students and practicing engineers to learn the finite-difference time-domain (FDTD) method and properly apply it toward their electromagnetic simulation projects. Each chapter contains a concise explanation of an essential concept and instruction on its implementation into computer code. Included projects increase in complexity, ranging from simulations in free space to propagation in dispersive media. This third edition utilizes the Python programming language, which is becoming the preferred computer language for the engineering and scientific community. Electromagnetic Simulation Using the FDTD Method with Python, Third Edition is written with the goal of enabling readers to learn the FDTD method in a manageable amount of time. Some basic applications of signal processing theory are explained to enhance the effectiveness of FDTD simulation. Topics covered in include one-dimensional simulation with the FDTD method, two-dimensional simulation, and three-dimensional simulation. The book also covers advanced Python features and deep regional hyperthermia treatment planning. Electromagnetic Simulation Using the FDTD Method with Python: Guides the reader from basic programs to complex, three-dimensional programs in a tutorial fashionIncludes a rewritten fifth chapter that illustrates the most interesting applications in FDTD and the advanced graphics techniques of PythonCovers peripheral topics pertinent to time-domain simulation, such as Z-transforms and the discrete Fourier transformProvides Python simulation programs on an accompanying website An ideal book for senior undergraduate engineering students studying FDTD, Electromagnetic Simulation Using the FDTD Method with Python will also benefit scientists and engineers interested in the subject.
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Practical Explainable AI Using Python
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decisionFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.What You'll LearnReview the different ways of making an AI model interpretable and explainableExamine the biasness and good ethical practices of AI modelsQuantify, visualize, and estimate reliability of AI modelsDesign frameworks to unbox the black-box modelsAssess the fairness of AI modelsUnderstand the building blocks of trust in AI modelsIncrease the level of AI adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
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Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty
This book introduces models and methodologies that can be employed towards making the Industry 4.0 vision a reality within the process industries, and at the same time investigates the impact of uncertainties in such highly integrated settings. Advances in computing power along with the widespread availability of data have led process industries to consider a new paradigm for automated and more efficient operations. The book presents a theoretically proven optimal solution to multi-parametric linear and mixed-integer linear programs and efficient solutions to problems such as process scheduling and design under global uncertainty. It also proposes a systematic framework for the uncertainty-aware integration of planning, scheduling and control, based on the judicious coupling of reactive and proactive methods. Using these developments, the book demonstrates how the integration of different decision-making layers and their simultaneous optimisation can enhance industrial process operations and their economic resilience in the face of uncertainty.
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New Developments in Multiple Objective and Goal Programming
This volumecomprisesthe proceedingsof the 8th InternationalConferenceon M- tiple Objective and Goal Programming: Theories and Applications (MOPGP08). ThisconferencewasheldinPortsmouth,UnitedKingdomonthe24th-26thSept- ber 2008. The conference was attended by 59 delegates. The countries represented were Belgium, Finland, France, Germany, Italy, Kuwait, Poland, Saudi Arabia, South Africa, Spain, Switzerland, United Kingdom, and United States. The MOPGP conference series is now a well-established discussion forum for practitioners and academics working in the eld of multiple objective and goal p- gramming. Conferences are held every 2 years, with previous conferences taking place in Portsmouth,UK in 1994;in Malaga,Spainin 1996;in QuebecCity, Canada in 1998; in Ustron, Poland in 2000; in Nara, Japan in 2002; in Hammamet, Tunisia in 2004; and in Tours, France in 2006. The selection of papers published in this volume re ects the breadth of the techniques and applications in the eld of multiple objective and goal progr- ming.Applications from the elds of supply chain management, nancial portfolio selection, nancial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, and the interface with data envelopment analysis give a good re ection of current usage. A pleasing variety of techniques are used, including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. As organisersof the MOGP08 conference, we thank all the participants and p- senters at the conference for helping the MOPGP series to continue to de ne and develop the state-of-the-artin the eld of multiple objective and goal programming.
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Programming for Problem Solving
This book provides an introduction to computer programming using Python as a way to solve problems. It focuses on programming concepts and fundamentals within the context of solving real-world problems. This work is licensed under the Creative Commons Attribution-Noncommercial-ShareAlike 4.0 Unported License. Copyright (c) 2018 Lenore Horowitz.
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