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Handbook of Logic and Proof Techniques for Computer Science
Logic is, and should be, the core subject area of modern mathemat ics. The blueprint for twentieth century mathematical thought, thanks to Hilbert and Bourbaki, is the axiomatic development of the subject. As a result, logic plays a central conceptual role. At the same time, mathematical logic has grown into one of the most recondite areas of mathematics. Most of modern logic is inaccessible to all but the special ist. Yet there is a need for many mathematical scientists-not just those engaged in mathematical research-to become conversant with the key ideas of logic. The Handbook of Mathematical Logic, edited by Jon Bar wise, is in point of fact a handbook written by logicians for other mathe maticians. It was, at the time of its writing, encyclopedic, authoritative, and up-to-the-moment. But it was, and remains, a comprehensive and authoritative book for the cognoscenti. The encyclopedic Handbook of Logic in Computer Science by Abramsky, Gabbay, and Maibaum is a wonderful resource for the professional. But it is overwhelming for the casual user. There is need for a book that introduces important logic terminology and concepts to the working mathematical scientist who has only a passing acquaintance with logic. Thus the present work has a different target audience. The intent of this handbook is to present the elements of modern logic, including many current topics, to the reader having only basic mathe matical literacy.
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Concepts and Semantics of Programming Languages 2
This book – composed of two volumes – explores the syntactical constructs of the most common programming languages, and sheds a mathematical light on their semantics, providing also an accurate presentation of the material aspects that interfere with coding.Concepts and Semantics of Programming Languages 2 presents an original semantic model, collectively taking into account all of the constructs and operations of modules and classes: visibility, import, export, delayed definitions, parameterization by types and values, extensions, etc. The model serves for the study of Ada and OCaml modules, as well as C header files. It can be deployed to model object and class features, and is thus used to describe Java, C++, OCaml and Python classes.This book is intended not only for computer science students and teachers but also seasoned programmers, who will find a guide to reading reference manuals and the foundations of program verification.
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Fundamentals of Engineering Programming with C and Fortran
Fundamentals of Engineering Programming with C and Fortran, first published in 1998, is a beginner's guide to problem solving with computers which shows how to quickly prototype a program for a particular engineering application. The book's side by side coverage of C and Fortran, the predominant computer languages in engineering, is unique. It emphasizes the importance of developing programming skills in C while carefully presenting the importance of maintaining a good reading knowledge of Fortran. Beginning with a brief description of computer architecture, the book then covers the fundamentals of computer programming for problem solving. It devotes separate chapters to data types and operators, control flow, type conversion, arrays, and file operations. The final chapter contains case studies that illustrate particular elements of modeling and visualization. Also included are a number of appendices covering C and Fortran language summaries and other useful topics. This concise and accessible book can be used as a text for introductory-level undergraduate courses on engineering programming or as a self-study guide for practising engineers.
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Hands-On Neuroevolution with Python
Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolutionKey FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook DescriptionNeuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems.You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones.By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.What you will learnDiscover the most popular neuroevolution algorithms – NEAT, HyperNEAT, and ES-HyperNEATExplore how to implement neuroevolution-based algorithms in PythonGet up to speed with advanced visualization tools to examine evolved neural network graphsUnderstand how to examine the results of experiments and analyze algorithm performanceDelve into neuroevolution techniques to improve the performance of existing methodsApply deep neuroevolution to develop agents for playing Atari gamesWho this book is forThis book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.
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Programming for Everyone
Programming for Everyone is designed to give the reader a general introduction to computer programming. And it's not just for those of you who are already comfortable with computer-speak; the book is written for a very general audience and focuses on providing you with a detailed understanding of the basic concepts. The book is also great for programmers who want to look into other areas (e.g. logic programming, computer graphics, games, etc.) they may not have experience in. Its main topics include general computer programming concepts, object-oriented programming fundamentals, developing web pages, developing 'apps' for mobile devices, application development for social network sites like Facebook, computer graphics and animation, computer security, and programming video games.
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Deep Learning with Python
"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You’ll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technologyMachine learning has made remarkable progress in recent years. We’ve gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. what's insideDeep learning from first principlesImage-classification, imagine segmentation, and object detectionDeep learning for natural language processingTimeseries forecastingNeural style transfer, text generation, and image generation about the readerReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the authorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
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Programming in Parallel with CUDA
CUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to a HPC facility. As a result, CUDA is increasingly important in scientific and technical computing across the whole STEM community, from medical physics and financial modelling to big data applications and beyond. This unique book on CUDA draws on the author's passion for and long experience of developing and using computers to acquire and analyse scientific data. The result is an innovative text featuring a much richer set of examples than found in any other comparable book on GPU computing. Much attention has been paid to the C++ coding style, which is compact, elegant and efficient. A code base of examples and supporting material is available online, which readers can build on for their own projects.
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Programming with TensorFlow
This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).
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Scaling Python with Ray
Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling. If your data processing has grown beyond what a single computer can handle, this book is for you.Written by experienced software architecture practitioners, Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. This book covers distributed processing (the pure Python implementation of serverless) and shows you how to:Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified platform for batch and streamingImplement advanced data processing with RayApply microservices with Ray platformImplement reliable Ray applications
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Hands-On Genetic Algorithms with Python
Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPyKey FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook DescriptionGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence.After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications.By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.What you will learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho this book is forThis book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.
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AI and ML for Coders in Pytorch
Eager to learn AI and machine learning but unsure where to start? Laurence Moroney's hands-on, code-first guide demystifies complex AI concepts without relying on advanced mathematics. Designed for programmers, it focuses on practical applications using PyTorch, helping you build real-world models without feeling overwhelmed.From computer vision and natural language processing (NLP) to generative AI with Hugging Face Transformers, this book equips you with the skills most in demand for AI development today. You'll also learn how to deploy your models across the web and cloud confidently.Gain the confidence to apply AI without needing advanced math or theory expertiseDiscover how to build AI models for computer vision, NLP, and sequence modeling with PyTorchLearn generative AI techniques with Hugging Face Diffusers and Transformers
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Easy Programming with the TI-99/4A
Welcome to the world of computer programming. Your Texas Instruments TI-99/4A is a real 16-bit microcomputer, complete with sound and color graphics. Making these features do what you want may seem impossible at fIrst, but don't worry; there's nothing mysterious about computer programming. Learning to program computers simply means learning a new language-in this case, TI BASIC (for Beginner's All purpose Symbolic Instruction Code), a version of the most popular language used on today's microcomputers. This book will teach you, step by step, how to tell your machine what you wish it to do-in other words, how to program it. But that's not all. You'll also find chapters on general microcomputer principles, cassette deck use, the TI-99/4A as a terminal for much larger systems, options for expansion, and a list of resources for getting the most out of your home computer. With the 99/4A you have access to a large library of programs, or software, already written by someone else. Without knowing anything about programming, you can use this software to play games, learn math, or store addresses. But at some point you'll want to make your personal computer really personal. You might want to create your own video game or compose a tune. And that's when you'll want to learn programming.
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Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. What You'll LearnReview data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithmUnderstand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networksGet acquainted with scikit-learn and PyTorchPredict sequences in recurrent neural networks and long short term memory Who This Book Is ForData scientists, machine learning engineers, and software professionals with basic skills in Python programming.
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Practical Data Science with Python
Learn to effectively manage data and execute data science projects from start to finish using PythonKey FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
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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.
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Machine Learning for Decision Sciences with Case Studies in Python
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning.This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
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Foundation Dynamic Web Pages with Python
Discover the concepts of creating dynamic web pages (HTML) with Python. This book reviews several methods available to serve up dynamic HTML including CGI, SSI, Django, and Flask. You will start by covering HTML pages and CSS in general and then move on to creating pages via CGI. It is easy to use and can serve as a foundation for the more advanced services available for launching dynamic web pages. Next you'll explore the SSI (Server Side Interface) method. This is a slightly more advanced interface included in mots web servers that adds functionality to modify static HTML pages to add such things as the current date or time, include additional HTML, and other features to a static web page before it is delivered to the user. The book also covers some of the key the Django module features, which must be added to the web server. These features include creating dynamic web pages and calling a database to provide additional information to the web page. Lastly you will explore the Flask module. While it has limited functionality on its own, it provides a very flexible environment to create a self designed system for delivery of dynamic web pages.By the time you finish this book, you will be able to choose the appropriate methodology for delivering dynamic information using fast HTML creation services.What You'll LearnUse HTML pages and CSS together to control the style of your web site.Install and configure SSI, Django, and Flask for Apache.Create dynamic web pages using CGI and creating a library of partial HTML pages to use in this task.Build dynamic web pages using SSI and auxiliary Python programs to enhance the SSI functionality.Develop dynamic web pages using Django.Who This Book Is ForSoftware Developers with basic Python programming skills interested in learning Web Development
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Handbook of Genetic Programming Applications
This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.
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Programming Quantum Computers
Quantum computers are poised to kick-start a new computing revolution—and you can join in right away. If you’re in software engineering, computer graphics, data science, or just an intrigued computerphile, this book provides a hands-on programmer’s guide to understanding quantum computing. Rather than labor through math and theory, you’ll work directly with examples that demonstrate this technology’s unique capabilities. Quantum computing specialists Eric Johnston, Nic Harrigan, and Mercedes Gimeno-Segovia show you how to build the skills, tools, and intuition required to write quantum programs at the center of applications. You’ll understand what quantum computers can do and learn how to identify the types of problems they can solve. This book includes three multichapter sections: Programming for a QPU—Explore core concepts for programming quantum processing units, including how to describe and manipulate qubits and how to perform quantum teleportation. QPU Primitives—Learn algorithmic primitives and techniques, including amplitude amplification, the Quantum Fourier Transform, and phase estimation. QPU Applications—Investigate how QPU primitives are used to build existing applications, including quantum search techniques and Shor’s factoring algorithm.
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Learn Robotics Programming
Gain experience of building a next-generation collaboration robotKey FeaturesGet up and running with the fundamentals of robotic programmingProgram a robot using Python and the Raspberry Pi 3Learn to build a smart robot with interactive and AI-enabled behaviorsBook DescriptionWe live in an age where the most difficult human tasks are now automated. Smart and intelligent robots, which will perform different tasks precisely and efficiently, are the requirement of the hour. A combination of Raspberry Pi and Python works perfectly when making these kinds of robots. Learn Robotics Programming starts by introducing you to the basic structure of a robot, along with how to plan, build, and program it. As you make your way through the book, you will gradually progress to adding different outputs and sensors, learning new building skills, and writing code for interesting behaviors with sensors. You'll also be able to update your robot, and set up web, phone, and Wi-Fi connectivity in order to control it. By the end of the book, you will have built a clever robot that can perform basic artificial intelligence (AI) operations.What you will learnConfigure a Raspberry Pi for use in a robotInterface motors and sensors with a Raspberry PiImplement code to make interesting and intelligent robot behaviorsUnderstand the first steps in AI behavior such as speech recognition visual processingControl AI robots using Wi-FiPlan the budget for requirements of robots while choosing partsWho this book is forLearn Robotics Programming is for programmers, developers, and enthusiasts interested in robotics and developing a fully functional robot. No major experience required just some programming knowledge would be sufficient.
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Handbook of Research on Tools for Teaching Computational Thinking in P-12 Education
While the growth of computational thinking has brought new awareness to the importance of computing education, it has also created new challenges. Many educational initiatives focus solely on the programming aspects, such as variables, loops, conditionals, parallelism, operators, and data handling, divorcing computing from real-world contexts and applications. This decontextualization threatens to make learners believe that they do not need to learn computing, as they cannot envision a future in which they will need to use it, just as many see math and physics education as unnecessary. The Handbook of Research on Tools for Teaching Computational Thinking in P-12 Education is a cutting-edge research publication that examines the implementation of computational thinking into school curriculum in order to develop creative problem-solving skills and to build a computational identity which will allow for future STEM growth. Moreover, the book advocates for a new approach to computing education that argues that while learning about computing, young people should also have opportunities to create with computing, which will have a direct impact on their lives and their communities. Featuring a wide range of topics such as assessment, digital teaching, and educational robotics, this book is ideal for academicians, instructional designers, teachers, education professionals, administrators, researchers, and students.
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Data Science and Analytics with Python
Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.
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Speed Up Your Python with Rust
Discover how to inject your code with highly performant Rust features to develop fast and memory-safe applicationsKey FeaturesLearn to implement Rust in a Python system without altering the entire systemWrite safe and efficient Rust code as a Python developer by understanding the essential features of RustBuild Python extensions in Rust by using Python NumPy modules in your Rust codeBook DescriptionPython has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in.This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust.By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support.What you will learnExplore the quirks of the Rust programming language that a Python developer needs to understand to code in RustUnderstand the trade-offs for multiprocessing and thread safety to write concurrent codeBuild and manage a software project with cargo and cratesFuse Rust code with Python so that Python can import and run Rust codeDeploy a Python Flask application in Docker that utilizes a private Rust pip moduleInspect and create your own Python objects in RustWho this book is forThis book is for Python developers who want to speed up their Python code with Rust and implement Rust in a Python system without altering the entire system. You'll be able to learn about all topics relating to Rust programming. Basic knowledge of Python is required to get the most out of this book.
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New Computer Consulting Handbook
Computer Consulting can be very lucrative. Start while working a full time job. Build Clients through the procedures described in this book. Learn the secrets that Today's Computer Consultants know. Learn where they go to solve the tough problems. Learn how to get $10,000 worth of software for less than $400.00. This book will explain the different business types that you can start. Learn the differences between business types. Learn the secrets on how to handle clients. Learn about selling computer hardware and what you need in order to sell computer hardware. This is the handbook that WILL get you started in the great field of Computer Consulting.
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Practical Handbook of Computerization for Distribution Managers
Back in 1983 I was chatting with Dick Coleman, publisher of Traffic World magazine, when he unexpectedly proposed that I write a column for the magazine on computer applications in the transportation/physical distribution industry. "But, Dick, I don't know all that much about computers," I protested. "You use one, don't you?" he asked logically. Yes, I did; I'd been running my consulting business with it for two years. But that didn't, I explained, make me an expert. "Think about it," he said. That's typical Coleman; he drops these studiedly casual ideas and just lets them lay there until you pick them up and wind up doing just what he wanted you to do all along. Sure enough, the longer I pondered the notion the more it appealed to me. OK, I wasn't a computer expert (I'm still not). But I was a computer user, in the transportation/distribution field; maybe from that perspective I might have some useful things to say to other transportation/distribution users and would-be users of computers. Thus was born the "Computer Software for Transportation" column. The first one appeared in the April 11, 1983, issue of Traffic World, and it's been a once-a-month schedule ever since. And thus, too, was ultimately born this book.
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IT Support Handbook
Become a more effective tech professional by learning how to provide the most useful IT support for your users. You'll learn how to efficiently and effectively deal with any type of problem, including operating systems, software, and hardware. IT support is often complex, time-consuming, and expensive, but it doesn't have to be with the right processes in place.Whether you're an individual, part of an IT support team, or managing staff supporting PC users in their homes, this book will help you understand the right way to approach, troubleshoot, and isolate problems so they can be handled efficiently, with the least disruption and cost to your business. Expanded and revised for this second edition, you'll make yourself popular with your colleagues, and keep your clients and users happy and productive.New To This EditionThe world of work has changed, with remote and hybrid working now the new normal and with IT Support departments worldwide having to adapt to this new paradigm. This expanded edition highlights the skills, tools, and structures needed to support businesses of any size in the new normal. What You'll LearnManage reporting, and keep a record of issues that occurProvide effective remote support for users working from home or elsewhere in the worldUse error and system reporting in Windows to obtain high-quality, relevant informationSpot patterns in user behavior that may be causing difficult-to-diagnose problemsBe familiar with best practices to make you a better support professional Who This Book Is ForIT professionals, IT support (on-site and remote), and system administrators who manage support teams. No prior knowledge is required.
R 1,368.00
Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 5: Logic Programming
The Handbook of Logic in Artificial Intelligence and Logic Programming is a multi-volume work covering all major areas of the application of logic to artificial intelligence and logic programming. The authors are chosen on an international basis and are leaders in the fields covered. Volume 5 is the last in this well-regarded series. Logic is now widely recognized as one of the foundational disciplines of computing. It has found applications in virtually all aspects of the subject, from software and hardware engineering to programming languages and artificial intelligence. In response to the growing need for an in-depth survey of these applications the Handbook of Logic in Artificial Intelligence and its companion, the Handbook of Logic in Computer Science have been created. The Handbooks are a combination of authoritative exposition, comprehensive survey, and fundamental research exploring the underlying themes in the various areas. Some mathematical background is assumed, and much of the material will be of interest to logicians and mathematicians. Volume 5 focuses particularly on logic programming. This book is intended for theoretical computer scientists.
R 19,183.64
Bioinformatics with Python Cookbook
Enhance your bioinformatics toolbox with practical Python recipes, tips, and tricks for key tasks like aligning sequence data, calling variants, and building Infrastructure as CodeKey FeaturesPerform sequence analysis at primary, secondary, and tertiary levels using Python librariesSolve real-world problems in the fields of phylogenetics, protein design, and annotationUse language models and other AI techniques to work with multimodal bioinformatics dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIf you've ever felt overwhelmed by the vast number of Python tools available for bioinformatics, you're not alone. The Bioinformatics with Python Cookbook is a recipe-based guide that explores practical approaches for solving classic bioinformatics challenges, showing you which Python packages work best for each task.You’ll start with the essential Python libraries for data science and bioinformatics, then move through key workflows in sequencing analysis, quality control, alignment, and variant calling. Along the way, you’ll pick up modern coding practices, explore recent advances in bioinformatics research, and gain hands-on experience with libraries such as NumPy, pandas, and sci-kit learn. This book walks you through core bioinformatics tasks such as phylogenetic analysis and population genomics while familiarizing you with the wealth of modern public bioinformatics databases. You’ll learn cloud computing approaches used by researchers, set up workflow orchestration systems for controlling bioinformatics pipelines, and see how AI and the use of large language models (LLMs) are reshaping the field–right down to designing proteins and DNA.By the end of this book, you’ll be ready to apply Python for real bioinformatics work and launch bioinformatics pipelines for your research.What you will learnProcess, analyze, and align sequencing dataCall variants and interpret their biological meaningUse modern cloud infrastructure to launch bioinformatics workflowsIngest, clean, and transform data efficientlyExplore how AI is shaping the future of bioinformaticsLeverage imaging data for biological insightsApply single-cell sequencing to cluster and compare gene expressionWho this book is forThis book is for early- to mid-level practitioners in bioinformatics, data science, and software engineering who want to improve their skills and apply practical solutions to real-world problems. You should have a basic understanding of biology, including DNA, proteins, and cell structure, as well as Python programming and software engineering techniques. While prior exposure to machine learning with Python is not essential, experience with a cloud computing platform (AWS, GCP, or Azure) will be helpful.
R 1,628.00
Practical Computer Vision with SimpleCV
SimpleCV is a cross platform (Windows, Macintosh, Linux) framework in Python that makes writing computer vision applications quick and easy. This book takes you through real world examples and applications using SimpleCV so that anyone, even with zero knowledge of computer vision, can use to create their own vision application in a few minutes with a webcam and PC. SimpleCV has its own integrated shell (command line) and code editor so you can rapidly test new techniques, load and run examples, view documentation, etc. The library also works with webcams, cell phones, the Xbox Kinect, and TI's Beagle Board and Panda Board. There are 1-click installers for every platform, so you can be up and running any of the examples included in this book in a few minutes.
R 865.00
Beginning Programming with C++ For Dummies
Learn to program with C++ quickly with this helpful For Dummies guide Beginning Programming with C++ For Dummies, 2nd Edition gives you plain-English explanations of the fundamental principles of C++, arming you with the skills and know-how to expertly use one of the world's most popular programming languages. You'll explore what goes into creating a program, how to put the pieces together, learn how to deal with standard programming challenges, and much more. Written by the bestselling author of C++ For Dummies, this updated guide explores the basic development concepts and techniques of C++ from a beginner's point of view, and helps make sense of the how and why of C++ programming from the ground up. Beginning with an introduction to how programming languages function, the book goes on to explore how to work with integer expressions and character expressions, keep errors out of your code, use loops and functions, divide your code into modules, and become a functional programmer. Grasp C++ programming like a pro, even if you've never written a line of codeMaster basic development concepts and techniques in C++Get rid of bugs and write programs that workFind all the code from the book and an updated C++ compiler on the companion website If you're a student or first-time programmer looking to master this object-oriented programming language, Beginning Programming with C++ For Dummies, 2nd Edition has you covered.
R 798.00
R 911.00
