Informatyka
Hassi Norlen
IBM Quantum Experience® is a leading platform for programming quantum computers and implementing quantum solutions directly on the cloud. This book will help you get up to speed with programming quantum computers and provide solutions to the most common problems and challenges.You’ll start with a high-level overview of IBM Quantum Experience® and Qiskit®, where you will perform the installation while writing some basic quantum programs. This introduction puts less emphasis on the theoretical framework and more emphasis on recent developments such as Shor’s algorithm and Grover’s algorithm. Next, you’ll delve into Qiskit®, a quantum information science toolkit, and its constituent packages such as Terra, Aer, Ignis, and Aqua. You’ll cover these packages in detail, exploring their benefits and use cases. Later, you’ll discover various quantum gates that Qiskit® offers and even deconstruct a quantum program with their help, before going on to compare Noisy Intermediate-Scale Quantum (NISQ) and Universal Fault-Tolerant quantum computing using simulators and actual hardware. Finally, you’ll explore quantum algorithms and understand how they differ from classical algorithms, along with learning how to use pre-packaged algorithms in Qiskit® Aqua.By the end of this quantum computing book, you’ll be able to build and execute your own quantum programs using IBM Quantum Experience® and Qiskit® with Python.
Quantum GIS. Tworzenie i analiza map
Bartłomiej Iwańczak
Twórz mapy i wykorzystuj je do swoich celów! Współczesny świat stawia przed nami wiele wyzwań. Nieustannie się dokształcamy, poznajemy nowe obszary wiedzy. Uczymy się wykorzystywać je do własnych celów. Coraz rzadziej zwracamy się do profesjonalistów z problemami, gdyż dzięki technologii jesteśmy w stanie poradzić sobie sami. Odkrywamy przy tym mnóstwo nowych, inspirujących aspektów życia. Dzięki tej książce można opanować podstawy bardzo przydatnej, choć do tej pory specjalistycznej dziedziny - kartografii i analizy danych przestrzennych. W dodatku bez dodatkowych kosztów - w darmowym, intuicyjnym programie Quantum GIS. Mapy potrzebne są wszystkim, nie tylko geografom! Jeśli pracujesz jako informatyk, logistyk, marketingowiec, dziennikarz, urzędnik czy architekt, prędzej czy później zechcesz przedstawić zgromadzone informacje w sposób wizualny, najlepiej na mapie. Dzięki tej książce bez większego trudu, a nawet z przyjemnością opanujesz zasady rysowania mapy, nanoszenia na nią obiektów według danych zawartych w tabeli, wyświetlania tych informacji, które są Ci potrzebne. Dowiesz się, jak planować trasę przewozu towarów, jak sprytnie policzyć budynki w każdej dzielnicy miasta czy jak najefektywniej rozsyłać ofertę handlową. Nauczysz się dowolnie zmieniać wygląd map, przekształcać je w obrazy i drukować lub umieszczać w Internecie. Nie jest to zwyczajny podręcznik. Wraz z tą książką będziesz krok po kroku zdobywać nowe umiejętności. Towarzyszyć Ci będzie młoda dziewczyna, Ula. Niejeden raz podsunie Ci użyteczną wskazówkę albo podpowie, co warto zapamiętać. Dzięki atrakcyjnej formie graficznej i ponad 300 ilustracjom zawsze zorientujesz się, gdzie w programie można znaleźć odpowiednie narzędzie. Analiza danych przestrzennych nie będzie miała dla Ciebie żadnych tajemnic. Do dzieła! Dzięki tej książce: ogarniesz wzrokiem przestrzeń i stworzysz mapę z Quantum GIS, poznasz serce współczesnej mapy w komputerze, zwiększysz użyteczność działania z pomocą narzędzi analitycznych QGIS. Odkryj dla siebie nową przestrzeń!
Jeremy Samuelson
Quantum computing is advancing rapidly, yet practical guidance for machine learning engineers remains limited. Most resources emphasize physics or theory, leaving practitioners unsure how quantum methods fit into real-world ML workflows. 'Quantum Machine Learning in Practice' addresses this gap with a hands-on, Python-first approach built for data scientists and ML engineers.Rather than presenting quantum models as replacements for classical ML, this book focuses on disciplined experimentation, hybrid architectures, and rigorous benchmarking. You will learn how classical data is encoded into quantum circuits, how variational models serve as classifiers and regressors, and how to evaluate quantum kernels and generative models responsibly. Concepts are grounded in simulator-based experiments using PennyLane, Qiskit, TensorFlow Quantum, and Cirq. Classical baselines are treated as first-class citizens throughout. You will design fair comparisons, analyze computational tradeoffs, and identify when classical ML remains superior. A complete end-to-end mini project reinforces transferable workflow skills, from problem framing through evaluation and interpretation.By the end, you will be able to design, implement, and critically assess hybrid quantum-classical machine learning systems with clarity and confidence.
Robert Loredo, Matt Broomhall
Prepare for a computing shift that will reshape security, optimization, and competitive advantage across every industry. This book helps you build a clear actionable approach to quantum readiness covering strategy, technology, talent, and risk in one framework.Written by Robert Loredo, Founder and CEO of Entangled Solutions Group, this book draws on 25 years of experience across IBM, academia, and enterprise innovation. As a former IBM Quantum Global Strategist, leader of the IBM Quantum Ambassador program, and holds over 275 patents, he translates this complex technology transition into decisions leaders can act on immediately.This book is organized as a complete strategic operating system for the quantum transition. Part One equips executives with investment frameworks, governance structures, and talent roadmaps. Part Two guides technical leaders through infrastructure assessment, workforce development, and quantum-era data strategy. Part Three delivers a security playbook covering post-quantum cryptography and adaptive architecture. Part Four maps the full quantum ecosystem and partnerships that will determine which institutions lead commercially.By the end, you’ll be able to define a quantum strategy, build the right capabilities, and lead your organization through quantum-driven change with clarity and control.
Quick Start Kubernetes. A Beginner's Guide to Container Orchestration in the Cloud - Third Edition
Nigel Poulton
This book is the backbone of modern cloud-native application deployment, but its complexity can be daunting for beginners. This book provides a practical and approachable guide to mastering Kubernetes, starting with fundamental concepts like microservices, orchestration, and cloud-native development. Readers will explore Kubernetes architecture, including control planes, worker nodes, and hosted solutions.Step-by-step instructions guide readers through setting up Kubernetes clusters on local and cloud platforms, containerizing applications, and pushing images to registries. Learn how to deploy containerized applications, connect them via services, and enable self-healing to ensure resilience.As you advance, discover how to scale applications dynamically, perform rolling updates for zero-downtime deployments, and troubleshoot real-world issues. The book concludes with resources for further learning, empowering readers to confidently manage Kubernetes environments in DevOps or cloud-native roles. Perfect for beginners, this hands-on guide simplifies Kubernetes for practical use.
R: Data Analysis and Visualization. Master the art of building analytical models using R
Tony Fischetti, Brett Lantz, Hrishi V. Mittal,...
The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility.The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework.With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs.The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions.Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on.
R Data Mining. Implement data mining techniques through practical use cases and real-world datasets
Enrico Pegoraro, Andrea Cirillo
R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet
PKS Prakash, Achyutuni Sri Krishna Rao
Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.