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Hands-On One-shot Learning with Python
Get to grips with building powerful deep learning models using PyTorch and scikit-learnKey FeaturesLearn how you can speed up the deep learning process with one-shot learningUse Python and PyTorch to build state-of-the-art one-shot learning modelsExplore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learningBook DescriptionOne-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples.Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence.By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.What you will learnGet to grips with the fundamental concepts of one- and few-shot learningWork with different deep learning architectures for one-shot learningUnderstand when to use one-shot and transfer learning, respectivelyStudy the Bayesian network approach for one-shot learningImplement one-shot learning approaches based on metrics, models, and optimization in PyTorchDiscover different optimization algorithms that help to improve accuracy even with smaller volumes of dataExplore various one-shot learning architectures based on classification and regressionWho this book is forIf you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.
R 1,498.00
Deep Learning Research Applications for Natural Language Processing
Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.
R 8,775.56
Building Modern CLI Applications in Go
Evolve the humble CLI using Go and unleash the next generation of powerful, flexible, and empathy-driven interfacesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesDiscover how Go enables the development of elegant and intuitive CLIsExplore a range of CLI development aspects and pick up a vast array of best practicesCreate engaging and user-friendly interfaces and learn how to distribute themBook DescriptionAlthough graphical user interfaces (GUIs) are intuitive and user-friendly, nothing beats a command-line interface (CLI) when it comes to productivity. Many organizations settle for a GUI without searching for alternatives that offer better accessibility and functionality. If this describes your organization, then pick up this book and get them to rethink that decision.Building Modern CLI Applications in Go will help you achieve an interface that rivals a GUI in elegance yet surpasses it in high-performance execution. Through its practical, step-by-step approach, you'll learn everything you need to harness the power and simplicity of the Go language to build CLI applications that revolutionize the way you work.After a primer on CLI standards and Go, you'll be launched into tool design and proper framework use for true development proficiency. The book then moves on to all things CLI, helping you master everything from arguments and flags to errors and API calls. Later, you'll dive into the nuances of empathic development so that you can ensure the best UX possible, before you finish up with build tags, cross-compilation, and container-based distribution.By the end of this UX book, you'll be fully equipped to take the performance and flexibility of your organization's applications to the next level.What you will learnMaster the Go code structure, testing, and other essentialsAdd a colorful dashboard to your CLI using engaging ASCII bannersUse Cobra, Viper, and other frameworks to give your CLI an edgeHandle inputs, API commands, errors, and timeouts like a proTarget builds for specific platforms the right way using build tagsBuild with empathy, using easy bug submission and tracebackContainerize, distribute, and publish your CLIs quickly and easilyWho this book is forThis book is for beginner- and intermediate-level Golang developers who take an interest in developing CLIs and enjoy learning by doing. You'll need an understanding of basic Golang programming concepts, but will require no prior knowledge of CLI design and development. This book helps you join a community of CLI developers and distribute within the popular Homebrew package management tool.
R 1,531.00
Applied Geospatial Data Science with Python
Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in PythonThe book includes colored images of important conceptsKey FeaturesLearn how to integrate spatial data and spatial thinking into traditional data science workflowsDevelop a spatial perspective and learn to avoid common pitfalls along the wayGain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expandedBook DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you'll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You'll learn how to read, process, and manipulate spatial data effectively. With data in hand, you'll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you'll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you'll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learnUnderstand the fundamentals needed to work with geospatial dataTransition from tabular to geo-enabled data in your workflowsDevelop an introductory portfolio of spatial data science work using PythonGain hands-on skills with case studies relevant to different industriesDiscover best practices focusing on geospatial data to bring a positive change in your environmentExplore solving use cases, such as traveling salesperson and vehicle routing problemsWho this book is forThis book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.
R 1,889.00
Building Low-Code Applications with Mendix
Transform your app ideas into fully functional prototypes with the help of expert tips and best practices from Mendix partnersKey FeaturesMeet the ever-increasing demand for software solution delivery without having to write any codeBuild high-availability, low-cost applications unlike those developed via a traditional software engineering approachExplore Mendix from product design through to delivery using real-world scenariosBook DescriptionLow-code is a visual approach to application development. It enables developers of varying experience levels to create web and mobile apps using drag-and-drop components and model-driven logic through a graphic user interface. Mendix is among the fastest-growing platforms that enable low-code enthusiasts to put their software ideas into practice without having to write much code, and Building Low-Code Applications with Mendix will help you get up and running with the process using examples and practice projects. The book starts with an introduction to Mendix, along with the reasons for using this platform and its tools for creating your first app. As you progress, you’ll explore Mendix Studio Pro, the visual environment that will help you learn Mendix app creation. Once you have your working app ready, you’ll understand how to enhance it with custom business logic and rules. Next, you’ll find out how to defend your app against bad data, troubleshoot and debug it, and finally, connect it with real-world business platforms. You’ll build practical skills as the book is filled with examples, real-world scenarios, and explanations of the tools needed to help you build low-code apps successfully. By the end of this book, you’ll have understood the concept of low-code development, learned how to use Mendix effectively, and developed a working app.What you will learnGain a clear understanding of what low-code development is and the factors driving its adoptionBecome familiar with the various features of Mendix for rapid application developmentDiscover concrete use cases of Studio ProBuild a fully functioning web application that meets your business requirementsGet to grips with Mendix fundamentals to prepare for the Mendix certification examUnderstand the key concepts of app development such as data management, APIs, troubleshooting, and debuggingWho this book is forThis book is for tech-savvy business analysts and citizen developers who want to get started with Mendix for rapid mobile and web application development. The book is also helpful for seasoned developers looking to learn a new tool/platform and for anyone passionate about designing technical solutions without wanting to indulge in the complexities of writing code. The book assumes beginner-level knowledge of object-oriented programming and the ability to translate technical solutions from business requirements.
R 1,433.00
Practical Computer Architecture with Python and ARM
Learn computer architecture with Python and ARM, simulating assembly program execution and designing a computer simulatorPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesBuild a computer simulator with Python: Learn computer architecture by designing and constructing a simulatorPython for architecture: Use Python to simulate and execute assembly language instructionsARM programming on Raspberry Pi: Explore ARM assembly language and run programs on Raspberry PiBook DescriptionThis comprehensive guide offers a unique and immersive learning experience by combining Python programming with ARM architecture.Starting with an introduction to computer architecture and the flow of data within a computer system, you’ll progress to building your own interpreter using Python. You’ll see how this foundation enables the simulation of computer operations and learn ways to enhance a simulator by adding new instructions and displaying improved results.As you advance, you’ll explore the TC1 Assembler and Simulator Program to gain insights into instruction analysis and explore practical examples of simulators. This will help you build essential skills in understanding complex computer instructions, strengthening your grasp of computer architecture. Moreover, you’ll be introduced to the Raspberry Pi operating system, preparing you to delve into the detailed language of the ARM computer. This includes exploring the ARM instruction set architecture, data-processing instructions, subroutines, and the stack.With clear explanations, practical examples, and coding exercises, this resource will enable you to design and construct your own computer simulator, simulate assembly language programs, and leverage the Raspberry Pi for ARM programming.What you will learnMaster the core principles of computer architectureUnderstand the role of registers, memory, and data flow in computersDiscover how to design and implement a computer simulator using PythonSimulate and execute assembly language programs on the simulatorEnhance the simulator using new instructions for improved outputAnalyze complex computer instructions for deeper architectural understandingExplore the ARM instruction set and data processing on the Raspberry PiDevelop proficiency in writing, assembling, and running ARM code on the Raspberry PiWho this book is forThis book is for university students studying computer science, particularly those enrolled in a computer architecture module. With its practical approach and succinct explanations, it is also suitable for hobbyists, enthusiasts, and self-learners seeking a deeper understanding of computer systems. The book assumes foundational knowledge of number bases, binary arithmetic, and Boolean logic concepts. While it primarily caters to the computer science field, this book is less geared toward electrical or electronics engineering.
R 1,368.00
R 306.00
Advanced Applications of NLP and Deep Learning in Social Media Data
Social media platforms are one of the main generators of textual data where people around the world share their daily life experiences and information with online society. The social, personal, and professional lives of people on these social networking sites generate not only a huge amount of data but also open doors for researchers and academicians with numerous research opportunities. This ample amount of data needs advanced machine learning, deep learning, and intelligent tools and techniques to receive, process, and interpret the information to resolve real-life challenges and improve the online social lives of people. Advanced Applications of NLP and Deep Learning in Social Media Data bridges the gap between natural language processing (NLP), advanced machine learning, deep learning, and online social media. It hopes to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. Covering topics such as machine learning-based prediction, emotion recognition, and high-dimensional text clustering, this premier reference source is an essential resource for OSN service providers, psychiatrists, psychologists, clinicians, sociologists, students and educators of higher education, librarians, researchers, and academicians.
R 7,926.65
Thoughtful Machine Learning with Python
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
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R 611.00
Hands-On Recommendation Systems with Python
With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the webKey FeaturesBuild industry-standard recommender systemsOnly familiarity with Python is requiredNo need to wade through complicated machine learning theory to use this bookBook DescriptionRecommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible..In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.What you will learnGet to grips with the different kinds of recommender systemsMaster data-wrangling techniques using the pandas libraryBuilding an IMDB Top 250 CloneBuild a content based engine to recommend movies based on movie metadataEmploy data-mining techniques used in building recommendersBuild industry-standard collaborative filters using powerful algorithmsBuilding Hybrid Recommenders that incorporate content based and collaborative flteringWho this book is forIf you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.
R 1,140.00
Introduction to Computational Models with Python
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website.The book’s five sections present: An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware toolsProgramming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithmsPython lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under LinuxImplementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from problem formulation to implementation of computational modelsThis book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.
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Programming Computer Vision with Python
If you want a basic understanding of computer vision's underlying theory and algorithms, this hands-on introduction is the ideal place to start. As a student, researcher, hacker, or enthusiast, you'll learn as you follow examples written in Python - the easy-to-learn language that has modules for handling images and mathematical computing and data mining on a par with commercial alternatives. Programming Computer Vision with Python teaches computer vision in broad terms that won't bog you down in theory. Instead, you'll find this book to be inspiring and motivating. You'll get all the code you need, with clear explanations on how to reproduce the book's examples and build upon them directly.
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Applying Math with Python
Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to use Python libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionThe updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What you will learnBecome familiar with basic Python packages, tools, and libraries for solving mathematical problemsExplore real-world applications of mathematics to reduce a problem in optimizationUnderstand the core concepts of applied mathematics and their application in computer scienceFind out how to choose the most suitable package, tool, or technique to solve a problemImplement basic mathematical plotting, change plot styles, and add labels to plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is forWhether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.
R 1,693.00
Machine Learning Concepts with Python and the Jupyter Notebook Environment
Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE.You’ll start by learning how to use Jupyter Notebooks to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Notebooks, you’ll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book.Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. What You Will LearnProgram in Python and TensorFlowTackle basic machine learning obstaclesDevelop in the Jupyter Notebooks environmentWho This Book Is ForIdeal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful.
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R 611.00
Mastering GUI Programming with Python
An advanced guide to creating powerful high-performance GUIs for modern, media-rich applications in various domains such as business and game developmentKey FeaturesGain comprehensive knowledge of Python GUI development using PyQt 5.12Explore advanced topics including multithreaded programming, 3D animation, and SQL databasesBuild cross-platform GUIs for Windows, macOS, Linux, and Raspberry PiBook DescriptionPyQt5 has long been the most powerful and comprehensive GUI framework available for Python, yet there is a lack of cohesive resources available for Python programmers to learn how to use it. This book will be your comprehensive guide to exploring GUI development with PyQt5. You will get started with an introduction to PyQt5, before going on to develop stunning GUIs with modern features. You will learn how to build forms using QWidgets and delve into important aspects of GUI development such as layouts, size policies, and event-driven programming. Moving ahead, you’ll discover PyQt5’s most powerful features through chapters on audio-visual programming with QtMultimedia, database-driven software with QtSQL, and web browsing with QtWebEngine. Next, in-depth coverage of multithreading and asynchronous programming will help you run tasks asynchronously and build high-concurrency processes with ease. In later chapters, you’ll gain insights into QOpenGLWidget, along with mastering techniques for creating 2D graphics with QPainter. You’ll also explore PyQt on a Raspberry Pi and interface it with remote systems using QtNetwork. Finally, you will learn how to distribute your applications using setuptools and PyInstaller. By the end of this book, you will have the skills you need to develop robust GUI applications using PyQt.What you will learnGet to grips with the inner workings of PyQt5Understand how elements in a GUI application communicate with signals and slotsStudy techniques for styling an applicationExplore database-driven applications with the QtSQL moduleCreate 2D graphics with QPainterDelve into 3D graphics with QOpenGLWidgetBuild network and web-aware applications with QtNetwork and QtWebEngineWho this book is forThis book is for programmers who want to create attractive, functional, and powerful GUIs using the Python language. You’ll also find this book useful if you are a student, professional, or anyone who wants to start exploring GUIs. Although prior knowledge of the Python language is assumed, experience with PyQt, Qt, or GUI programming is not required.
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R 1,003.45
R 611.00
Programming Google App Engine with Python
This practical guide shows intermediate and advanced web and mobile app developers how to build highly scalable Python applications in the cloud with Google App Engine. The flagship of Google's Cloud Platform, App Engine hosts your app on infrastructure that grows automatically with your traffic, minimizing up-front costs and accommodating unexpected visitors. You'll learn hands-on how to perform common development tasks with App Engine services and development tools, including deployment and maintenance. App Engine's Python support includes a fast Python 2.7 interpreter, the standard library, and a WSGI-based runtime environment. Choose from many popular web application frameworks, including Django and Flask.Get a hands-on introduction to App Engine's tools and features, using an example application Simulate App Engine on your development machine with tools from Google Cloud SDK Structure your app into individually addressable modules, each with its own scaling configuration Exploit the power of the scalable Cloud Datastore, using queries, transactions, and data modeling with the ndb library Use Cloud SQL for standard relational databases with App Engine applications Learn how to deploy, manage, and inspect your application on Google infrastructure
R 1,557.00
Building Serverless Microservices in Python
A practical guide for developing end-to-end serverless microservices in Python for developers, DevOps, and architects.Key FeaturesCreate a secure, cost-effective, and scalable serverless data APIUse identity management and authentication for a user-specific and secure web applicationGo beyond traditional web hosting to explore the full range of cloud hosting optionsBook DescriptionOver the last few years, there has been a massive shift from monolithic architecture to microservices, thanks to their small and independent deployments that allow increased flexibility and agile delivery. Traditionally, virtual machines and containers were the principal mediums for deploying microservices, but they involved a lot of operational effort, configuration, and maintenance. More recently, serverless computing has gained popularity due to its built-in autoscaling abilities, reduced operational costs, and increased productivity.Building Serverless Microservices in Python begins by introducing you to serverless microservice structures. You will then learn how to create your first serverless data API and test your microservice. Moving on, you'll delve into data management and work with serverless patterns. Finally, the book introduces you to the importance of securing microservices.By the end of the book, you will have gained the skills you need to combine microservices with serverless computing, making their deployment much easier thanks to the cloud provider managing the servers and capacity planning.What you will learnDiscover what microservices offer above and beyond other architecturesCreate a serverless application with AWSGain secure access to data and resourcesRun tests on your configuration and codeCreate a highly available serverless microservice data APIBuild, deploy, and run your serverless configuration and codeWho this book is forIf you are a developer with basic knowledge of Python and want to learn how to build, test, deploy, and secure microservices, then this book is for you. No prior knowledge of building microservices is required.
R 1,140.00
Scientific Computing with Python
Leverage this example-packed, comprehensive guide for all your Python computational needsKey FeaturesLearn the first steps within Python to highly specialized conceptsExplore examples and code snippets taken from typical programming situations within scientific computing.Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing.Book DescriptionPython has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.What you will learnUnderstand the building blocks of computational mathematics, linear algebra, and related Python objectsUse Matplotlib to create high-quality figures and graphics to draw and visualize resultsApply object-oriented programming (OOP) to scientific computing in PythonDiscover how to use pandas to enter the world of data processingHandle exceptions for writing reliable and usable codeCover manual and automatic aspects of testing for scientific programmingGet to grips with parallel computing to increase computation speedWho this book is forThis book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.
R 1,498.00
R 482.00
Internet of Things with Python
Interact with the world and rapidly prototype IoT applications using PythonAbout This Book• Rapidly prototype even complex IoT applications with Python and put them to practical use• Enhance your IoT skills with the most up-to-date applicability in the field of wearable tech, smart environments, and home automation• Interact with hardware, sensors, and actuators and control your DIY IoT projects through PythonWho This Book Is ForThe book is ideal for Python developers who want to explore the tools in the Python ecosystem in order to build their own IoT applications and work on IoT-related projects. It is also a very useful resource for developers with experience in other programming languages that want to easily prototype IoT applications with the Intel Galileo Gen 2 board.What You Will Learn• Prototype and develop IoT solutions from scratch with Python as the programming language• Develop IoT projects with Intel Galileo Gen 2 board along with Python• Work with the different components included in the boards using Python and the MRAA library• Interact with sensors, actuators, and shields• Work with UART and local storage• Interact with any electronic device that supports the I2C bus• Allow mobile devices to interact with the board• Work with real-time IoT and cloud services• Understand Big Data and IoT analyticsIn DetailInternet of Things (IoT) is revolutionizing the way devices/things interact with each other. And when you have IoT with Python on your side, you'll be able to build interactive objects and design them.This book lets you stay at the forefront of cutting-edge research on IoT. We'll open up the possibilities using tools that enable you to interact with the world, such as Intel Galileo Gen 2, sensors, and other hardware. You will learn how to read, write, and convert digital values to generate analog output by programming Pulse Width Modulation (PWM) in Python. You will get familiar with the complex communication system included in the board, so you can interact with any shield, actuator, or sensor.Later on, you will not only see how to work with data received from the sensors, but also perform actions by sending them to a specific shield. You'll be able to connect your IoT device to the entire world, by integrating WiFi, Bluetooth, and Internet settings. With everything ready, you will see how to work in real time on your IoT device using the MQTT protocol in python.By the end of the book, you will be able to develop IoT prototypes with Python, libraries, and tools.Style and approach This book takes a tutorial-like approach with mission critical chapters. The initial chapters are introductions that set the premise for useful examples covered in later chapters.
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Machine Learning with Spark and Python
Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code. Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
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R 1,099.00
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 LearnUnderstand machine learning with streaming data conceptsReview incremental and online learningDevelop models for detecting concept driftExplore techniques for classification, regression, and ensemble learning in streaming data contextsApply best practices for debugging and validating machine learning models in streaming data contextGet introduced to other open-source frameworks for handling streaming data.Who This Book Is ForMachine learning engineers and data science professionals
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Statistical Learning with Math and Python
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs.As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter.This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
R 1,649.00
Programming with Python for Social Scientists
As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:the fundamentals of why and how to do your own programming in social scientific research,questions of ethics and research design,a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
R 5,706.92
Machine Learning for Financial Risk Management with Python
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models.Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models.Review classical time series applications and compare them with deep learning modelsExplore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learningRevisit and improve market risk models (VaR and expected shortfall) using machine learning techniquesDevelop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML modelsCapture different aspects of liquidity with a Gaussian mixture modelUse machine learning models for fraud detectionIdentify corporate risk using the stock price crash metricExplore a synthetic data generation process to employ in financial risk
R 2,052.00
Building Object Applications that Work
Written by internationally acclaimed trainer and developer Scott Ambler, this book is a must-have resource for designers, programmers and testers of today's OO applications. The author takes you through the entire process of building object applications, from analyzing the project (so you know what you need to build), to designing a user-friendly interface, to testing your approaches to make sure your applications work properly. Building Object Applications That Work includes sections on: • how to use the Unified Modeling Language effectively • analysis, so you know what you need to build • design techniques, so that you know how you are going to build your application • collecting the right metrics to improve your development approach • applying OO patterns to improve the quality of your applications • creating applications for stand-alone, client/server and distributed environments • using both relational and object-oriented databases to make your objects persistent • architecting your applications so they are maintainable and extensible.
R 3,707.49
R 611.00
Extending Power BI with Python and R
Perform more advanced analysis and manipulation of your data beyond what Power BI can do to unlock valuable insights using Python and RKey FeaturesGet the most out of Python and R with Power BI by implementing non-trivial codeLeverage the toolset of Python and R chunks to inject scripts into your Power BI dashboardsImplement new techniques for ingesting, enriching, and visualizing data with Python and R in Power BIBook DescriptionPython and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages.You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model.By the end of this book, you'll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.What you will learnDiscover best practices for using Python and R in Power BI productsUse Python and R to perform complex data manipulations in Power BIApply data anonymization and data pseudonymization in Power BILog data and load large datasets in Power BI using Python and REnrich your Power BI dashboards using external APIs and machine learning modelsExtract insights from your data using linear optimization and other algorithmsHandle outliers and missing values for multivariate and time-series dataCreate any visualization, as complex as you want, using R scriptsWho this book is forThis book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.
R 1,889.00
Hands-On Markov Models with Python
Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.What you will learnExplore a balance of both theoretical and practical aspects of HMMImplement HMMs using different datasets in Python using different packagesUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problemsDevelop a Bayesian approach to inference in HMMsImplement HMMs in finance, natural language processing (NLP), and image processingDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithmWho this book is forHands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data.Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book
R 1,335.00
Programming with Python for Social Scientists
As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:the fundamentals of why and how to do your own programming in social scientific research,questions of ethics and research design,a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
R 3,287.32
UML 2003 -- The Unified Modeling Language, Modeling Languages and Applications
Thepastyearhasbeenaneventfuloneforthoseinterestedinsoftwaremodeling. The ?rst major revision of the Uni?ed Modeling Language, UML2.0, is in the process of adoption by the Object Management Group (OMG), and it makes many long-desired additions and improvements to UML. At the same time, it expands what was already a large language. A challenge for both practitioners andresearchersistohelpsmooththeadoptionofthisnewlanguage.Increasingly, attention is being paid to the use of specialized languages, often pro?les of UML, appropriate for di?erent purposes; this is one way to make UML less overwh- ming. Accordingly, the focus of the UML conference is gradually expanding from UML to software modeling in general. Simultaneously, model-driven development is being pursued as a way of - creasing the bene?ts from modeling throughout the software development p- cess. Gradually, it is developing from a set of slogans into a reality. Many of the papers in this volume are concerned, directly or indirectly, with how to make modeling, rather than coding, the heart of software development, and how to realize the resulting bene?ts of higher-level thinking. Much work remains to be done.
R 2,015.00
R 611.00
Scientific Scripting with Python
Learn how to automate your scientific research with Python. This book teaches you the basics of the popular Python scripting language, before delving deep into all aspects of processing data. Python is a simple, yet powerful, programming language similar to Perl, Tcl, and Ruby. It is heavily used in scientific research, and by companies like Google. This book places a strong emphasis on practical techniques for handling data. Although targeted specifically at scientists and engineers, anyone who needs to automate their work flow would find it useful.
R 1,215.56
OpenCV 4 with Python Blueprints
Get to grips with traditional computer vision algorithms and deep learning approaches, and build real-world applications with OpenCV and other machine learning frameworksKey FeaturesUnderstand how to capture high-quality image data, detect and track objects, and process the actions of animals or humansImplement your learning in different areas of computer visionExplore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented realityBook DescriptionOpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks.You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks.By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.What you will learnGenerate real-time visual effects using filters and image manipulation techniques such as dodging and burningRecognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensorLearn feature extraction and feature matching to track arbitrary objects of interestReconstruct a 3D real-world scene using 2D camera motion and camera reprojection techniquesDetect faces using a cascade classifier and identify emotions in human faces using multilayer perceptronsClassify, localize, and detect objects with deep neural networksWho this book is forThis book is for intermediate-level OpenCV users who are looking to enhance their skills by developing advanced applications. Familiarity with OpenCV concepts and Python libraries, and basic knowledge of the Python programming language are assumed.
R 1,661.00
Hands-On GPU Computing with Python
Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda AccelerateKey FeaturesUnderstand effective synchronization strategies for faster processing using GPUsWrite parallel processing scripts with PyCuda and PyOpenCLLearn to use the CUDA libraries like CuDNN for deep learning on GPUsBook DescriptionGPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing.This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance.By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly.What you will learnUtilize Python libraries and frameworks for GPU accelerationSet up a GPU-enabled programmable machine learning environment on your system with AnacondaDeploy your machine learning system on cloud containers with illustrated examplesExplore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.Perform data mining tasks with machine learning models on GPUsExtend your knowledge of GPU computing in scientific applicationsWho this book is forData Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.
R 1,498.00
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.
R 3,883.00
Digital Signal Processing and Applications with the OMAP - L138 eXperimenter
Teaches digital signal processing concepts via hands-on examples The OMAP-L138 eXperimenter is the latest inexpensive DSP development system to be adopted by the Texas Instruments University Program. The OMAP-L138 processor contains both ARM and DSP cores and is aimed at portable and mobile multimedia applications. This book concentrates on the demonstration of real-time DSP algorithms implemented on its C6748 DSP core. Digital Signal Processing and Applications with the OMAP-L138 eXperimenter provides an extensive and comprehensive set of program examples to aid instructors in teaching DSP in a laboratory using audio frequency signals—making it an ideal text for DSP courses at senior undergraduate and postgraduate levels. Subjects covered include polling-based, interrupt-based, and DMA-based I/O methods, and how real-time programs may be run using the board support library (BSL), the DSP/BIOS real-time operating system, or the DSP/BIOS Platform Support Package. Chapters include: Analog input and output with the OMAP-L138 eXperimenter Finite impulse response filters Infinite impulse response filters Fast Fourier transform Adaptive filters DSP/BIOS and platform support package Each chapter begins with a review of background theory and then presents a number of real-time program examples to reinforce understanding of that theory and to demonstrate the use of the OMAP-L138 eXperimenter and Texas Instruments Code Composer Studio integrated development environment.
R 5,207.00
Machine Learning for Time Series Forecasting with Python
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
R 1,368.00
Building Vue.js Applications with GraphQL
Take your Vue.js knowledge to the next level by understanding full-stack development concepts and exploring modern web technologies such as AWS Amplify, GraphQL, and Quasar FrameworkKey FeaturesBuild a fully functional Vue.js web app and learn how it integrates with GraphQLTransform your chat application into a Progressive Web Application (PWA) for web deploymentDiscover practical recipes, exploring the capabilities of the GraphQL API for full-stack development using Quasar FrameworkBook DescriptionSince its release by Facebook in 2012, GraphQL has taken the internet by storm. Huge companies such as Airbnb and Audi have started to adopt it, while small to medium-sized companies are now recognizing the potential of this query-based API. GraphQL may seem strange at first, but as you start to read about and experience more of it, you won't want to use REST APIs anymore. With the recipes in this book, you will learn how to build a complete real-time chat app from scratch. Starting by creating an AWS Amplify environment, you will delve into developing your first GraphQL Schema. You will then learn how to add the AppSync GraphQL client and create your first GraphQL mutation. The book also helps you to discover the simplicity and data fetching capabilities of GraphQL that make it easy for front-end developers to communicate with the server. You will later understand how to use Quasar Framework to create application components and layouts. Finally, you will find out how to create Vuex modules in your application to manage the app state, fetch data using the GraphQL client, and deploy your application to the web. By the end of this book, you'll be well versed in proof-of-concept full-stack applications that explore the power of GraphQL with AWS Amplify, and you'll be able to use Quasar Framework to create your Vue applications.What you will learnSet up your Vue.js projects with Vue CLI and explore the power of Vue componentsDiscover steps to create functional components in Vue.js for faster renderingBecome familiar with AWS Amplify and learn how to set up your environmentUnderstand how to create your first GraphQL schemaUse Quasar Framework to create simple and effective interfacesDiscover effective techniques to create queries for interacting with dataExplore Vuex for adding state management capabilities to your appDiscover techniques to deploy your applications effectively to the webWho this book is forThis book is for intermediate-level Vue.js developers who want to take their first step toward full-stack development. Prior knowledge of Vue.js and JavaScript is required before getting started with this book.
R 1,335.00
Hands-On Bitcoin Programming with Python
Simplified Python programming for Bitcoin and blockchainKey FeaturesBuild Bitcoin applications in Python with the help of simple examplesMine Bitcoins, program Bitcoin-enabled APIs and transaction graphs, and build trading botsAnalyze Bitcoin transactions and produce visualizations using Python data analysis toolsBook DescriptionBitcoin is a cryptocurrency that’s changing the face of online payments. Hands-On Bitcoin Programming with Python teaches you to build software applications for mining and creating Bitcoins using Python.This book starts with the basics of both Bitcoin and blockchain and gives you an overview of these inherent concepts by showing you how to build Bitcoin-driven applications with Python. Packed with clear instructions and practical examples, you will learn to understand simple Python coding examples that work with this cryptocurrency.By the end of the book, you’ll be able to mine Bitcoins, accept Bitcoin payments on the app, and work with the basics of blockchain technology to create simply distributed ledgers.What you will learnMaster the Bitcoin APIs in Python to manipulate Bitcoin from your Python appsBuild your own Bitcoin trading bots to buy Bitcoins at a lower price and sell them at a higher priceWrite scripts to process Bitcoin payments through a website or appDevelop software for Bitcoin mining to create Bitcoin currency on your own computer hardwareCreate your own keys, addresses, and wallets in Python codeWrite software to analyze Bitcoin transactions and produce reports, graphs, and other visualizationsWho this book is forHands-On Bitcoin Programming with Python consists of examples that will teach you to build your own Bitcoin application. You will learn to write scripts, build software for mining, and create Bitcoins using Python. Anyone with prior Python experience, who wants to explore Python Bitcoin programming and start building Bitcoin-driven Python apps, will find this book useful.
R 879.00
R 1,139.00
