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Coding For Dummies, All New Edition
Your first step toward thinking like a coder Coding For Dummies is one of the most popular get-started guides for new coders. This all- new edition demystifies essential coding concepts before diving into building simple apps in Python and JavaScript. For anyone brand-new to writing code, this book is an ideal first step, because you'll get an overview of how coding works, the essential tools of coding, and how to apply coding in different ways. Plus, you'll get insight into how new tools like AI can quickly boost your coding know-how. Enter the world of code with this easy-to-follow Dummies guide! Learn the basics of coding, including key concepts you'll need in every coding languageGet a rundown of the most popular language, and start coding in JavaScript and PythonFollow step-by-step instructions to build your first appsDiscover how AI can help you become a better, faster coder Looking for a foundation in the basics of coding, plus simple tasks to help you get started? Coding For Dummies is the book for you.
R 730.00
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Multi-Agent Programming
Multi-Agent Programming is an essential reference for anyone interested in the most up-to-date developments in MAS programming. While previous research has focused on the development of formal and informal approaches to analyze and specify Multi-Agent Systems, this book focuses on the development of programming languages and tools which not only support MAS programming, but also implement key concepts of MAS in a unified framework.Part I describes approaches that rely on computational logic or process algebra – Jason, 3APL, IMPACT, and CLAIM/SyMPA. Part II presents languages and platforms that extend or are based on Java – JADE, Jadex and JACKTM. Part III provides two significant industry specific applications – The DEFACTO System for coordinating human-agent teams for disaster response, and the ARTIMIS rational dialogue agent technology. Also featured are seven appendices for quick reference and comparison.
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Multi-Agent Programming
Multi-Agent Programming is an essential reference for anyone interested in the most up-to-date developments in MAS programming. While previous research has focused on the development of formal and informal approaches to analyze and specify Multi-Agent Systems, this book focuses on the development of programming languages and tools which not only support MAS programming, but also implement key concepts of MAS in a unified framework.Part I describes approaches that rely on computational logic or process algebra – Jason, 3APL, IMPACT, and CLAIM/SyMPA. Part II presents languages and platforms that extend or are based on Java – JADE, Jadex and JACKTM. Part III provides two significant industry specific applications – The DEFACTO System for coordinating human-agent teams for disaster response, and the ARTIMIS rational dialogue agent technology. Also featured are seven appendices for quick reference and comparison.
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Multi-Agent Search under Uncertainty
Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by the researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditionsMulti-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasksDeep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviorsAlgorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readingsTheoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development.
R 5,210.00
Hands-On Natural Language Processing with Python
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlowKey FeaturesWeave neural networks into linguistic applications across various platformsPerform NLP tasks and train its models using NLTK and TensorFlowBoost your NLP models with strong deep learning architectures such as CNNs and RNNsBook DescriptionNatural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow.By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.What you will learnImplement semantic embedding of words to classify and find entitiesConvert words to vectors by training in order to perform arithmetic operationsTrain a deep learning model to detect classification of tweets and newsImplement a question-answer model with search and RNN modelsTrain models for various text classification datasets using CNNImplement WaveNet a deep generative model for producing a natural-sounding voiceConvert voice-to-text and text-to-voiceTrain a model to convert speech-to-text using DeepSpeechWho this book is forHands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.
R 1,498.00
Programming Microsoft Dynamics 365 Business Central
Extend Microsoft Business Central with custom functionality using the AL language in Visual Studio Code, guided by practical examples, expert insights, and real-world use casesKey FeaturesWork with the new additions to the AL Language, Visual Studio Code, and Business CentralEnhance your AL programming skills through real-world examples and best practices curated by industry expertsImplement updated best practices for development to build efficient, reliable, and maintainable extensionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionBusiness Central opens a world of endless possibilities for custom business logic and functionality through extensions and customizations. For beginners in the AL language, navigating the vast landscape can feel overwhelming. It’s challenging to know where to start and what’s essential to learn. This updated edition offers a concise and well-organized guide, featuring hands-on exercises to support a successful learning journey.You’ll get to grips with the basics of Business Central and how to create your first Visual Studio Code project with the AL language. As you learn about the table data structure, simple and complex data types, relationships, validation, and data flow, you’ll be able to add and extend tables to your project. Progressively, you'll uncover the intricacies of user interfaces with pages, create advanced Excel report layouts, and harness the power of data queries. Immersing yourself in the AL language syntax, you’ll start with variables, methods, procedures, and statements. You'll explore advanced topics to create business logic such as FlowFields, filtering, CRUD methods, interactions between object types, and different API interfaces that can be created using the AL language.By the end of this book, you’ll be equipped to build fully featured, robust extensions and custom capabilities for Business Central.What you will learnSet up your first AL development environment with Visual Studio CodeUnderstand the data structure and flow of Business CentralDesign, build, and extend Table, Page, Report, Codeunit, Query, and XMLport objects in Business CentralDevelop AL code for business logic and data manipulation in Visual Studio CodeUse Excel layouts to report Business Central dataIntegrate Business Central with external systems using API web servicesImplement multi-language in your own extensionDebug and troubleshoot Business Central applicationsWho this book is forThis book is for anyone who wants to learn about Microsoft Dynamics 365 Business Central's powerful and extensive development capabilities. ERP consultants and managers of Business Central development will also find this book helpful. Although you aren't expected to have previously worked with Microsoft Dynamics Business Central, having a basic understanding of programming and familiarity with business applications software will help you understand the concepts covered in this book.
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Learning Functional Programming
Learn how to think and write code like a functional programmer. With this practical guide, software developers familiar with object-oriented programming will dive into the core concepts of functional programming and learn how to use both functional and OOP features together on large or complex software projects.Author Jack Widman uses samples from Java, Python, C#, Scala, and JavaScript to help you gain a new perspective and a set of tools for managing the complexity in your problem domain. You'll be able to write code that's simpler, reusable, easier to test and modify, and more consistently correct. This book also shows you how to use patterns from category theory to help bridge the gap between OOP and functional programming.Learn functional programming fundamentals and explore the way functional programmers approach problemsUnderstand how FP differs from object-oriented and imperative programmingUse a set of practical, applicable design patterns that model reality in a functional wayLearn how to incorporate FP and OOP features into software projectsApply functional design patterns appropriately and use them to write correct, robust, and easily modifiable code
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R 580.00
Python for Probability, Statistics, and Machine Learning
Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
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Essential Programming for the Technical Artist
This book is based on a successful curriculum designed to elevate technical artists with no programming experience up to essential programming competency as quickly as possible. Instead of abstract, theoretical problems, the curriculum employs familiar applications encountered in real production environments to demonstrate each lesson.Written with artists in mind, this book introduces novice programmers to the advantageous world of Python programming with relevant and familiar examples. Any digital artists (not just technical artists) will find this book helpful in assisting with day-to-day production activities.Concentrating upon subjects relevant to the creation of computer graphic assets, this book introduces Python basics, functions, data types, object-oriented programming, exception handling, file processing, graphical user interface creation, PEP 8 standards, and regular expressions. Programming within the SideFX Houdini 3D animation software provides a familiar environment for artists to create and experiment with the covered Python topics.
R 3,260.00
Raspberry Pi Supercomputing and Scientific Programming
Build an inexpensive cluster of multiple Raspberry Pi computers and install all the required libraries to write parallel and scientific programs in Python 3. This book covers setting up your Raspberry Pis, installing the necessary software, and making a cluster of multiple Pis. Once the cluster is built, its power has to be exploited by means of programs to run on it. So, Raspberry Pi Supercomputing and Scientific Programming teaches you to code the cluster with the MPI4PY library of Python 3. Along the way, you will learn the concepts of the Message Passing Interface (MPI) standards and will explore the fundamentals of parallel programming on your inexpensive cluster. This will make this book a great starting point for supercomputing enthusiasts who want to get started with parallel programming. The book finishes with details of symbolic mathematics and scientific and numerical programming in Python, using SymPi, SciPy, NumPy, and Matplotlib. You’ll see how to process signals and images, carry out calculations using linear algebra, and visualize your results, all using Python code. With the power of a Raspberry Pi supercomputer at your fingertips, data-intensive scientific programming becomes a reality at home.What You Will Learn Discover the essentials of supercomputingBuild a low-cost cluster of Raspberry Pis at homeHarness the power of parallel programming and the Message Passing Interface (MPI)Use your Raspberry Pi for symbolic, numerical, and scientific programmingWho This Book Is ForPython 3 developers who seek the knowledge of parallel programming, Raspberry Pi enthusiasts, researchers, and the scientific Python community.
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R 611.00
Beyond Vibe Coding
AI is transforming software development, shifting programmers from writing code to collaborating with AI in an intent-driven workflow-this is vibe coding. Vibe Coding: The Future of Programming explores how AI-powered coding assistants like GitHub Copilot and OpenAI Codex are reshaping the way we build software, from automating routine coding tasks to influencing architecture and design decisions.Written by Addy Osmani, this guide provides developers, tech leads, and organizations with practical strategies to integrate AI into their workflows effectively. Learn how to refine AI-generated code, master prompt engineering, and explore advanced techniques like model fine-tuning and multi-agent coding systems. Whether you're adopting AI tools today or preparing for the future of software engineering, this book offers insights and hands-on examples to keep your skills sharp in this evolving landscape.Understand how AI-assisted development is reshaping programmingMaster techniques for refining, validating, and debugging AI-generated codeExplore multi-agent coding systems and AI-driven software workflowsFuture-proof your career by adapting to AI's growing role in development
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Foundations of Parallel Programming
The major reason for the lack of use of parallel computing is the mismatch between the complexity and variety of parallel hardware, and the software development tools to program it. The cost of developing software needs to be amortised over decades, but the platforms on which it executes change every few years, requiring complete rewrites. The evident cost-effectiveness of parallel computation has not been realized because of this mismatch. This book presents an integrated approach to parallel software development by addressing both software and performance issues together. It presents a methodology for software construction that produces architecture-independent and intellectually abstract software. The software can execute efficiently on a range of existing and potential hardware configurations. The approach is based on the construction of categorical data types, a generalization of abstract data types, and of objects. Categorical data types abstract both from the representation of a data type, and also from the detailed control flow necessary to perform operations on it. They thus impose a strong separation between the semantics, on which programs can depend, and the implementation, which is therefore free to hide the parallel machine properties that are used.
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Programming Machine Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.
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Introduction to Numerical Programming
Makes Numerical Programming More Accessible to a Wider AudienceBearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author’s many years of practical research and teaching experience to offer a systematic approach to relevant programming concepts. Adopting a practical, broad appeal, this user-friendly book offers guidance to anyone interested in using numerical programming to solve science and engineering problems. Emphasizing methods generally used in physics and engineering—from elementary methods to complex algorithms—it gradually incorporates algorithmic elements with increasing complexity.Develop a Combination of Theoretical Knowledge, Efficient Analysis Skills, and Code Design Know-HowThe book encourages algorithmic thinking, which is essential to numerical analysis. Establishing the fundamental numerical methods, application numerical behavior and graphical output needed to foster algorithmic reasoning, coding dexterity, and a scientific programming style, it enables readers to successfully navigate relevant algorithms, understand coding design, and develop efficient programming skills. The book incorporates real code, and includes examples and problem sets to assist in hands-on learning. Begins with an overview on approximate numbers and programming in Python and C/C++, followed by discussion of basic sorting and indexing methods, as well as portable graphic functionalityContains methods for function evaluation, solving algebraic and transcendental equations, systems of linear algebraic equations, ordinary differential equations, and eigenvalue problemsAddresses approximation of tabulated functions, regression, integration of one- and multi-dimensional functions by classical and Gaussian quadratures, Monte Carlo integration techniques, generation of random variables, discretization methods for ordinary and partial differential equations, and stability analysisThis text introduces platform-independent numerical programming using Python and C/C++, and appeals to advanced undergraduate and graduate students in natural sciences and engineering, researchers involved in scientific computing, and engineers carrying out applicative calculations.
R 5,965.07
Python Illustrated
Learn Python coding concepts the fun way through humour, storytelling, and beginner-friendly examples that make your first step into the world of Python programming enjoyableKey FeaturesFollow an adorable, illustrated teaching cat with sharp coding instincts as your guideBuild confidence and coding skills with step-by-step explanations that gradually increase your understandingReinforce your learning through mini-projects and exercises at the end of every chapterPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis is not your average Python programming book, because the world doesn’t need another one of those. Instead, it’s an illustrated, fun, and hands-on guide that treats learning Python like the adventure it should be. It’s designed especially for beginners who want to understand how code works without getting overwhelmed. You’ll be guided by a cheeky, know-it-all cat who’s surprisingly good at teaching Python from scratch. Don’t worry about going through it alone; a slightly moody dachshund dog is your study buddy, learning right alongside you. Each chapter introduces a core programming concept, explains it with a playful twist, and reinforces it through human-friendly examples, analogies, and exercises. Whether you’re a software professional or someone who’s never written a single line of code, this book will help you build real Python coding skills… and even enjoy the process (shocking, right?). Forget dry tutorials and walls of text. Python Illustrated speaks to visual learners, creative thinkers, cat lovers, dog lovers, and anyone who prefers their learning with a dash of humor. From writing your first function to understanding object-oriented programming, you’ll build a solid foundation in Python (without the usual headaches). What you will learnSet up Python and write your first programUnderstand variables, data types, and conditional statementsUse loops, lists, dictionaries, and tuples to handle data efficientlyWrite and use functions to create reusable codeManage files and understand object-oriented programming and inheritanceDebug your Python code to ensure it works flawlesslyDiscover how you can keep learning and building beyond this bookWho this book is forThis book is ideal for anyone with a sense of humour who’s new to Python or programming in general. It takes a step-by-step hands-on approach that will work for anyone with a practical mindset. Whether you're a student or a professional developer looking to expand your skill set, this book offers a comprehensive and enjoyable learning experience.
R 1,303.00
Training Systems Using Python Statistical Modeling
Leverage the power of Python and statistical modeling techniques for building accurate predictive modelsKey FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook DescriptionPython's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics.You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them.By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.What you will learnUnderstand the importance of statistical modelingLearn about the various Python packages for statistical analysisImplement algorithms such as Naive Bayes, random forests, and moreBuild predictive models from scratch using Python's scikit-learn libraryImplement regression analysis and clusteringLearn how to train a neural network in PythonWho this book is forIf you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.
R 1,335.00
