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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.
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R 1,450.13
Mastering Django ORM
Unlock Django ORM's full potential with advanced performance tuning, query optimization, and scalable Python solutions. Most Django developers love the simplicity of the ORM--until performance issues hit. Suddenly, the "magic" feels like a black box: queries slow down, joins multiply, and scaling becomes painful. Mastering Django ORM: Performance Tuning and Advanced Query Optimization lifts the lid on Django's Object Relational Mapper and shows you what's really happening under the hood. You'll learn how queries are built, how to spot hidden inefficiencies, and how to optimize them for production systems. Instead of treating the ORM as a mystery, this book gives you the confidence to bend it to your will--writing cleaner, faster, and more scalable code. Drawing from real-world experience, author Afsal MS shares lessons learned from costly mistakes, practical fixes, and advanced techniques that will transform the way you work with Django ORM. Whether you're building a startup MVP or maintaining a high-traffic enterprise system, this guide will help you unlock the full potential of Django ORM and take your Python web applications to the next level. What You Will Learn Understand Django ORM internals and how queries are constructed.Write efficient queries that scale with growing data.Spot and fix common ORM-related performance bottlenecks.Choose between ORM features and raw SQL when appropriate.Apply advanced query patterns for real-world business problems.Optimize Django ORM for high-performance and scalability.Leverage Django 6.0+ features and async ORM capabilities.Use profiling and debugging tools to monitor and improve query performance. Who this Book Is For Developers using Django who want to move beyond treating the ORM as a black box. It's ideal for those building or maintaining production applications and seeking practical strategies to optimize queries, improve performance, and scale effectively.
R 1,140.00
Continuous Architecture in Practice
Update Your Architectural Practices for New Challenges, Environments, and Stakeholder Expectations "I am continuously delighted and inspired by the work of these authors. Their first book laid the groundwork for understanding how to evolve the architecture of a software-intensive system, and this latest one builds on it in some wonderfully actionable ways." --Grady Booch, Chief Scientist for Software Engineering, IBM Research Authors Murat Erder, Pierre Pureur, and Eoin Woods have taken their extensive software architecture experience and applied it to the practical aspects of software architecture in real-world environments. Continuous Architecture in Practice provides hands-on advice for leveraging the continuous architecture approach in real-world environments and illuminates architecture’s changing role in the age of Agile, DevOps, and cloud platforms. This guide will help technologists update their architecture practice for new software challenges. As part of the Vaughn Vernon Signature Series, this title was hand-selected for the practical, delivery-oriented knowledge that architects and software engineers can quickly apply. It includes in-depth guidance for addressing today's key quality attributes and cross-cutting concerns such as security, performance, scalability, resilience, data, and emerging technologies. Each key technique is demonstrated through a start-to-finish case study reflecting the authors’ deep experience with complex software environments. Key topics include: Creating sustainable, coherent systems that meet functional requirements and the quality attributes stakeholders care aboutUnderstanding team-based software architecture and architecture as a "flow of decisions"Understanding crucial issues of data management, integration, and change, and the impact of varied data technologies on architectureArchitecting for security, including continuous threat modeling and mitigationArchitecting for scalability and resilience, including scaling microservices and serverless environmentsUsing architecture to improve performance in continuous delivery environmentsUsing architecture to apply emerging technologies successfully Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
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R 10,195.89
Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision.Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
R 10,420.70
Function/Architecture Optimization and Co-Design of Embedded Systems
Function Architecture Co-Design is a new paradigm for the design and implementation of embedded systems. Function/Architecture Optimization and Co-Design of Embedded Systems presents the authors' work in developing a function/architecture optimization and co-design formal methodology and framework for control-dominated embedded systems. The approach incorporates both data flow and control optimizations performed on a suitable novel intermediate design task representation. The aim is not only to enhance productivity of the designer and system developer, but also to improve quality of the final synthesis outcome. Function/Architecture Optimization and Co-Design of Embedded Systems discusses the proposed function/architecture co-design methodology, focusing on design representation, optimization, validation, and synthesis. Throughout the text, the difference between behavior specification and implementation is emphasized. The current need in co-design to move from synthesis-based technology to compiler-based technology is pointed out. The authors describe and show how performing data flow and control optimizations at the high abstraction level can lead to significant size and performance improvements in both the synthesized hardware and software. The work builds on bodies of research in the silicon and software compilation domains. The aforementioned techniques are specialized to the embedded systems domain. It is recognized that guided optimization can be applied on the internal design representation, no matter what the abstraction level, and need not be restricted to the final stages of software assembly code generation, or hardware synthesis. Function/Architecture Optimization and Co-Design of Embedded Systems will be of primary interest to researchers, developers, and professionals in the field of embedded systems design.
R 3,032.01
Practical Guide to Quantum Machine Learning and Quantum Optimization
Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guideKey FeaturesGet a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisitesLearn the process of implementing the algorithms on simulators and actual quantum computersSolve real-world problems using practical examples of methodsBook DescriptionThis book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. You'll be introduced to quantum computing using a hands-on approach with minimal prerequisites.You'll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms, and you will find out how to solve optimization problems with quantum annealing, QAOA, Grover Adaptive Search (GAS), and VQE. This book also shows you how to train quantum machine learning models, such as quantum support vector machines, quantum neural networks, and quantum generative adversarial networks. The book takes a straightforward path to help you learn about quantum algorithms, illustrating them with code that's ready to be run on quantum simulators and actual quantum computers. You'll also learn how to utilize programming frameworks such as IBM's Qiskit, Xanadu's PennyLane, and D-Wave's Leap.Through reading this book, you will not only build a solid foundation of the fundamentals of quantum computing, but you will also become familiar with a wide variety of modern quantum algorithms. Moreover, this book will give you the programming skills that will enable you to start applying quantum methods to solve practical problems right away.What you will learnReview the basics of quantum computingGain a solid understanding of modern quantum algorithmsUnderstand how to formulate optimization problems with QUBOSolve optimization problems with quantum annealing, QAOA, GAS, and VQEFind out how to create quantum machine learning modelsExplore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLaneDiscover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interfaceWho this book is forThis book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Basic knowledge of linear algebra and some programming skills (for instance, in Python) are assumed, although all mathematical prerequisites will be covered in the appendices.
R 1,824.00
Practical and Experimental Robotics
Taking a completely hands-on approach, using cheap and easily available robotics kits, Practical and Experimental Robotics provides a detailed exploration of the construction, theory, and experiments for different types of robots. With topics ranging from basic stamp microcontrollers to biped and propeller based robots, the text contains laboratory experiments, examples with solutions, and case studies. The authors begin with a review of the essential elements of electronics and mechanics. They describe the basic mechanical construction and electrical control of the robot, then give at least one example of how to operate the robot using microcontrollers or software. The book includes a reference chapter on Basic Stamp Microcontollers with example code pieces and a chapter completely devoted to PC interfacing. Each chapter begins with the fundamentals, then moves on to advanced topics, thus building a foundation for learning from the ground up. Building a bridge between technicians who have hands-on experience and engineers with a deeper insight into the workings, the book covers a range of machines, from arm, wheel, and leg robots to flying robots and robotic submarines and boats. Unlike most books in this field, this one offers a complete set of topics from electronics, mechanics, and computer interface and programming, making it an independent source for knowledge and understanding of robotics.
R 9,978.17
Model-Driven Architecture in Practice
This book introduces all the relevant information required to understand and put Model Driven Architecture (MDA) into industrial practice. It clearly explains which conceptual primitives should be present in a system specification, how to use UML to properly represent this subset of basic conceptual constructs, how to identify just those diagrams and modeling constructs that are actually required to create a meaningful conceptual schema, and how to accomplish the transformation process between the problem space and the solution space. The approach is fully supported by commercially available tools.
R 1,694.14
Learn Quantum Computing with Python and IBM Quantum Experience
A step-by-step guide to learning the implementation and associated methodologies in quantum computing with the help of the IBM Quantum Experience, Qiskit, and Python that will have you up and running and productive in no timeKey FeaturesDetermine the difference between classical computers and quantum computersUnderstand the quantum computational principles such as superposition and entanglement and how they are leveraged on IBM Quantum Experience systemsRun your own quantum experiments and applications by integrating with QiskitBook DescriptionIBM Quantum Experience is a platform that enables developers to learn the basics of quantum computing by allowing them to run experiments on a quantum computing simulator and a real quantum computer. This book will explain the basic principles of quantum mechanics, the principles involved in quantum computing, and the implementation of quantum algorithms and experiments on IBM's quantum processors. You will start working with simple programs that illustrate quantum computing principles and slowly work your way up to more complex programs and algorithms that leverage quantum computing. As you build on your knowledge, you'll understand the functionality of IBM Quantum Experience and the various resources it offers. Furthermore, you'll not only learn the differences between the various quantum computers but also the various simulators available. Later, you'll explore the basics of quantum computing, quantum volume, and a few basic algorithms, all while optimally using the resources available on IBM Quantum Experience. By the end of this book, you'll learn how to build quantum programs on your own and have gained practical quantum computing skills that you can apply to your business.What you will learnExplore quantum computational principles such as superposition and quantum entanglementBecome familiar with the contents and layout of the IBM Quantum ExperienceUnderstand quantum gates and how they operate on qubitsDiscover the quantum information science kit and its elements such as Terra and AerGet to grips with quantum algorithms such as Bell State, Deutsch-Jozsa, Grover's algorithm, and Shor's algorithmHow to create and visualize a quantum circuitWho this book is forThis book is for Python developers who are looking to learn quantum computing and put their knowledge to use in practical situations with the help of IBM Quantum Experience. Some background in computer science and high-school-level physics and math is required.
R 1,498.00
Practical Deep Learning for Cloud and Mobile
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly. Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi Learn by building examples such as Silicon Valley's "Not Hotdog," image search engines, and your own mini-autonomous car Use transfer learning to train models in minutes Optimize your apps to run efficiently on different hardware Discover strategies to scale up from a single user to millions Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology
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Time Series Analysis with Python Cookbook
Perform time series analysis and forecasting confidently with this Python code bank and reference manual. Access exclusive GitHub bonus chapters and hands-on recipes covering Python setup, probabilistic deep learning forecasts, frequency-domain analysis, large-scale data handling, databases, InfluxDB, and advanced visualizations.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook DescriptionTo use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.What you will learnUnderstand what makes time series data different from other dataApply imputation and interpolation strategies to handle missing dataImplement an array of models for univariate and multivariate time seriesPlot interactive time series visualizations using hvPlotExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsUse conformal prediction for constructing prediction intervals for time seriesWho this book is forThis book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want to learn time series analysis and forecasting techniques step by step through practical Python recipes. To get the most out of this book, you’ll need fundamental Python programming knowledge. Prior experience working with time series data to solve business problems will help
R 1,954.00
Pro Android Python with SL4A
Pro Android Python with SL4A is for programmers and hobbyists who want to write apps for Android devices without having to learn Java first. Paul Ferrill leads you from installing the Scripting Layer for Android (SL4A) to writing small scripts, to more complicated and interesting projects, and finally to uploading and packaging your programs to an Android device.Android runs scripts in many scripting languages, but Python, Lua, and Beanshell are particularly popular. Most programmers know more than one programming language, so that they have the best tool for whatever task they want to accomplish. Pro Android Python with SL4A explores the world of Android scripting by introducing you to the most important open-source programming languages that are available on Android-based hardware. Pro Android Python with SL4A starts by exploring the Android software development kit and then shows you how to set up an Eclipse-based Android development environment. You then approach the world of Android programming by using Beanshell, which runs on the Dalvik, and learning how to write small programs to administer an Android device.Next, discover how Lua, a lightweight language perfectly suited for scripting on smaller devices, can work with Android. Lua can be used for small but important tasks, like SMS encryption and synchronizing photos with flickr. Last, but certainly not least, you will discover the world of Python scripting for SL4A, and the power contained within the full range of Python modules that can combine with the Android SDK. You'll learn to write small location-aware apps to get you started, but by the end of this book, you'll find yourself writing fully GUI-fied applications running on the Android desktop!Pro Android Python with SL4A is rounded out with a chapter on distributing and packaging scripts, a skill that you'll find very useful as you reach out to a wider audience with your programs.
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R 763.00
Numerical Computing with Python
Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is forIf you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.
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R 3,044.00
Designing Information Architecture
"A fresh, updated perspective on Information Architecture (IA), blending foundational principles with modern insights to help you design intuitive, structured, and human-centered digital experiences." - Jim Kalbach, Chief Evangelist at MuralGet your hands on a well-structured, easy-reference handbook filled with IA best practices for organizing digital information spacesKey FeaturesLearn IA from Pabini Gabriel-Petit, UX expert and founder of UXmattersGet a practical introduction to IA in the broader context of UX research and designGain expert insights from industry leaders on IA’s evolution, techniques, and applicationsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn a world that suffers from information overload, how can information architects help people quickly find the exact digital content they need? This is where Designing Information Architecture comes in as your practical guide to creating easy-to-use experiences for digital information spaces—be it websites, applications, or intranets—by creating well-structured information architectures (IAs) and effective navigation and search systems. It shows you how to improve the organization, findability, and usability of digital content using proven IA design methods and strategies.Designing Information Architecture is an up-to-date resource on IA. Written by Pabini Gabriel-Petit, a recognized expert in user experience (UX) and IA with decades of industry experience, this book offers both expert insights and practical design guidance. It also explores modern, AI-driven approaches to implementing search systems that can help users overcome the challenges of information overload.Throughout the book, you’ll learn why a well-structured information architecture remains more critical than ever in delivering effective digital information spaces.What you will learnInformation-seeking models, strategies, tactics, and behaviorsPrinciples for designing IAs that support human cognitive and visual capabilitiesWayfinding principles for placemaking, orientation, navigation, labeling, and searchUseful structural patterns and information-organization schemesUX research methods and analytics for information architectureContent analysis, modeling, and mapping methodsCategorizing content and creating controlled vocabulariesDesigning and mapping information architecturesLeveraging artificial intelligence (AI) to deliver optimal search resultsWho this book is forThis complete reference is for both experienced and aspiring information architects and UX design professionals who are looking to create effective information architectures for digital information spaces, including Web sites, applications, and intranets. It is also a valuable resource for members of product teams—especially developers, product managers, and other UX professionals who collaborate closely with information architects—and other stakeholders who want to understand and support the information-architecture workflow.
R 1,465.00
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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R 546.68
Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision.Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
R 7,924.74
Practical Computer Vision Applications Using Deep Learning with CNNs
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. What You Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applicationsWho This Book Is ForData scientists, machine learning and deep learning engineers, software developers.
R 3,182.25
VLSI Analog Circuits: Algorithms, Architecture, Modeling, and Circuit Implementation, Second Edition
Complete coverage of VLSI signal processing circuits—from scientific theory to design and practical applicationsThis comprehensive electrical engineering textbook offers in-depth discussions on the analysis, design, and application of VLSI analog filters and circuits in microelectronic systems and signal processing environments. The book shows how VLSI circuit techniques can be applied to solve real-world design problems that demand the scaling down of device geometries while meeting cost, power, function, reliability, and other constraints. VLSI Analog Circuits: Algorithm, Architecture, Modeling, and Circuit Implementation has evolved from a course the author teaches at Arizona State University, where the material presented in the book has been classroom tested for more than 10 years. After a detailed overview of VLSI technology, you will learn how to build accurate circuit models and solve practical design problems. Mixed-mode circuit configurations, RF signal processing, and digital-based analog signal processing circuits are thoroughly covered. · In-depth coverage makes this book highly suitable both as a textbook and a professional reference· Instructor materials include lecture PowerPoint slides and homework/project assignments · Written by an experienced electrical engineering educator and VLSI expert
R 4,691.10
Practical AI for Business Leaders, Product Managers, and Entrepreneurs
Most economists agree that AI is a general purpose technology (GPT) like the steam engine, electricity, and the computer. AI will drive innovation in all sectors of the economy for the foreseeable future. Practical AI for Business Leaders, Product Managers, and Entrepreneurs is a technical guidebook for the business leader or anyone responsible for leading AI-related initiatives in their organization. The book can also be used as a foundation to explore the ethical implications of AI. Authors Alfred Essa and Shirin Mojarad provide a gentle introduction to foundational topics in AI. Each topic is framed as a triad: concept, theory, and practice. The concept chapters develop the intuition, culminating in a practical case study. The theory chapters reveal the underlying technical machinery. The practice chapters provide code in Python to implement the models discussed in the case study. With this book, readers will learn: The technical foundations of machine learning and deep learning How to apply the core technical concepts to solve business problems The different methods used to evaluate AI models How to understand model development as a tradeoff between accuracy and generalization How to represent the computational aspects of AI using vectors and matrices How to express the models in Python by using machine learning libraries such as scikit-learn, statsmodels, and keras
R 1,322.00
Hands-On Predictive Analytics with Python
Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook DescriptionPredictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.What you will learnGet to grips with the main concepts and principles of predictive analyticsLearn about the stages involved in producing complete predictive analytics solutionsUnderstand how to define a problem, propose a solution, and prepare a datasetUse visualizations to explore relationships and gain insights into the datasetLearn to build regression and classification models using scikit-learnUse Keras to build powerful neural network models that produce accurate predictionsLearn to serve a model's predictions as a web applicationWho this book is forThis book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.
R 1,661.00
Developing Graphics Frameworks with Python and OpenGL
Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds.You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.
R 2,528.00
Dynamic System Modelling and Analysis with MATLAB and Python
Dynamic System Modeling & Analysis with MATLAB & Python A robust introduction to the advanced programming techniques and skills needed for control engineering In Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers, accomplished control engineer Dr. Jongrae Kim delivers an insightful and concise introduction to the advanced programming skills required by control engineers. The book discusses dynamic systems used by satellites, aircraft, autonomous robots, and biomolecular networks. Throughout the text, MATLAB and Python are used to consider various dynamic modeling theories and examples. The author covers a range of control topics, including attitude dynamics, attitude kinematics, autonomous vehicles, systems biology, optimal estimation, robustness analysis, and stochastic system. An accompanying website includes a solutions manual as well as MATLAB and Python example code. Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers provides readers with a sound starting point to learning programming in the engineering or biology domains. It also offers: A thorough introduction to attitude estimation and control, including attitude kinematics and sensors and extended Kalman filters for attitude estimationPractical discussions of autonomous vehicles mission planning, including unmanned aerial vehicle path planning and moving target trackingComprehensive explorations of biological network modeling, including bio-molecular networks and stochastic modelingIn-depth examinations of control algorithms using biomolecular networks, including implementation Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers is an indispensable resource for advanced undergraduate and graduate students seeking practical programming instruction for dynamic system modeling and analysis using control theory.
R 4,396.00
Practical Microservices with Dapr and .NET
Use the innovative, highly portable event-driven distributed application runtime to simplify building resilient and scalable microservices for cloud and edge applications.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key FeaturesBuild resilient, stateless, and stateful microservice applications that run on the cloud and edgeOvercome common issues in distributed systems, such as low latency and scaling, using any language and frameworkLearn how to expose and operate Dapr applications with multiple optionsBook DescriptionThis second edition will help you get to grips with microservice architectures and how to manage application complexities with Dapr in no time. You'll understand how Dapr simplifies development while allowing you to work with multiple languages and platforms. Following a C# sample, you'll understand how Dapr's runtime, building blocks, and software development kits (SDKs) help you to simplify the creation of resilient and portable microservices.Dapr provides an event-driven runtime that supports the essential features you need for building microservices, including service invocation, state management, and publish/subscribe messaging. You'll explore all of those in addition to various other advanced features with this practical guide to learning Dapr. With a focus on deploying the Dapr sample application to an Azure Kubernetes Service cluster and to the Azure Container Apps serverless platform, you'll see how to expose the Dapr application with NGINX, YARP, and Azure API Management.By the end of this book, you'll be able to write microservices easily by implementing industry best practices to solve problems related to distributed systems.What you will learnUse Dapr to create services, invoking them directly and via pub/subDiscover best practices for working with microservice architecturesLeverage the actor model to orchestrate data and behaviorExpose API built with Dapr applications via NGINX and Azure API ManagementUse Azure Kubernetes Service to deploy a sample applicationMonitor Dapr applications using Zipkin, Prometheus, and GrafanaScale and load test Dapr applications on KubernetesGet to grips with Azure Container Apps as you combine Dapr with a serverless platformWho this book is forThis book is for developers looking to explore and implement microservices architectures in Dapr applications using .NET examples. Whether you are new to microservices or have knowledge of this architectural approach and want to get hands-on experience using Dapr, you'll find this book useful. Familiarity with .NET will help you to understand the C# samples and code snippets used in the book.
R 1,433.00
Data Structures and Program Design Using Python
Data structures provide a means to managing huge amounts of information such as large databases, using SEO effectively, and creating Internet/Web indexing services. This book is designed to present fundamentals of data structures for beginners using the Python programming language in a friendly, self-teaching, format. Practical analogies using real world applications are integrated throughout the text to explain technical concepts. The book includes a variety of end-of-chapter practice exercises, e.g., programming, theoretical, and multiple-choice.FEATURES:Covers data structure fundamentals using PythonNumerous tips, analogies, and practical applications enhance understanding of subjects under discussion"Frequently Asked Questions" integrated throughout the text clarify and explain conceptsIncludes a variety of end-of-chapter exercises, e.g., programming, theoretical, and multiple choice.
R 1,823.00
R 2,277.00
Practical Serverless and Microservices with C#
Take a realistic look at microservices and distributed systems with the .NET stack to understand the limitations of microservices development through a practical lensKey FeaturesWork through common scenarios encountered when developing distributed microservices applicationsUnderstand cost considerations, traffic limits, and time limits surrounding serverless environmentsTake full advantage of the synergy between Azure services (Container Apps, Functions, and Aspire) and .NET codePurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionFrom the authors of the Software Architecture with C# and .NET series comes this practical and grounded showcase of microservices using the .NET stack. Written for .NET developers entering the world of modern cloud and distributed applications, it shows you when microservices and serverless architectures are the right choice for building scalable enterprise solutions and when they’re not. You’ll gain a realistic understanding of their use cases and limitations. Rather than promoting microservices as a one-size-fits-all solution, it encourages thoughtful adoption based on real-world needs.Following a brief introduction and important setup, the book helps you prepare for practical application through examples such as a ride-sharing website. You’ll work with Docker, Kubernetes, Azure Container Apps, and the new .NET Aspire with considerations for security, observability, and cost management. The book culminates in a complete event-driven application that brings together everything you've covered.By the end of the book, you’ll have a well-rounded understanding of cloud and distributed .NET—viewed through the lens of two industry veterans.What you will learnSet up serverless environments in Azure for developing and debuggingDesign reliable communication and computation across microservicesExplore Azure Functions in depth and use triggers for IoT and background tasksUse Azure Container Apps to simplify the creation and management of containersApply best practices to secure a microservices applicationAccurately assess and calculate costs and usage limits in serverless solutionsWho this book is forThis book is for engineers and senior software developers looking to advance into modern cloud and distributed applications. It helps professionals evolve their knowledge of microservices and serverless architecture to get the best of both architectural models. Prior experience with C#/.NET and the Microsoft Stack (Entity Framework and ASP.NET Core) is required to get the most out of this book.If you’ve enjoyed the authors’ previous Software Architecture with C# and .NET series, this new book offers an in-depth exploration of select topics in those earlier works.
R 1,693.00
Practical Blockchains and Cryptocurrencies
Create cryptocurrency and blockchain applications by examining the key algorithms and concepts pertaining to blockchains, transaction processing, mining, distributed consensus, and anonymous currencies. In this book, you’ll develop a fully functional cryptocurrency from scratch in the Python language. Practical Blockchains and Cryptocurrencies is a reference for development of blockchain applications and provides you with rigorous information on cryptography and the theory underlying blockchains. This book consists of small chapters that focus on particular topics. You’ll start with a short history of money. Next, you will survey the bitcoin and altcoin ecosystem before delving into cryptographic hash functions, symmetric encryption, public key cryptography, and digital signatures. All the mathematics required to develop blockchain applications is covered. The emphasis is on providing a lucid and rigorous exposition on the nature and working of these constructs. The next major segment of the book discusses the key concepts and algorithms required to develop blockchain and cryptocurrency applications. There are expositions on blockchain construction, Merkle trees, peer-to-peer networks, cryptocurrency addresses, transactions, and mining. You’ll take a deep dive into the formation of consensus in distributed systems.In this book you’ll develop a fully functional cryptocurrency called Helium from scratch in Python. The language requirements are modest since it is presumed that most readers will not be acquainted with Python. The entire source code and unit test code is included in this book.Practical Blockchains and Cryptocurrencies interleaves theory and Helium program code chapters in order to demonstrate the practical application of theory in working Helium program code. What You Will Learn Gain the mathematical foundations as well as the concepts and algorithms of blockchains and cryptocurrenciesImplement a cryptocurrency from scratch in PythonMaster the design of distributed blockchain applications Who This Book Is For Anyone interested in creating cryptocurrency and blockchain applications
R 1,824.00
Scalable Data Architecture with Java
Orchestrate data architecting solutions using Java and related technologies to evaluate, recommend and present the most suitable solution to leadership and clientsKey FeaturesLearn how to adapt to the ever-evolving data architecture technology landscapeUnderstand how to choose the best suited technology, platform, and architecture to realize effective business valueImplement effective data security and governance principlesBook DescriptionJava architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data.This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert.You'll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you'll understand how to architect a batch and real-time data processing pipeline. You'll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you'll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics.By the end of this book, you'll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients.What you will learnAnalyze and use the best data architecture patterns for problemsUnderstand when and how to choose Java tools for a data architectureBuild batch and real-time data engineering solutions using JavaDiscover how to apply security and governance to a solutionMeasure performance, publish benchmarks, and optimize solutionsEvaluate, choose, and present the best architectural alternativesUnderstand how to publish Data as a Service using GraphQL and a REST APIWho this book is forData architects, aspiring data architects, Java developers and anyone who wants to develop or optimize scalable data architecture solutions using Java will find this book useful. A basic understanding of data architecture and Java programming is required to get the best from this book.
R 1,596.00
