Название: Simplified Quantum Computing with Applications
Автор: Koji Nagata, Do Ngoc Diep, Ahmed Farouk, Tadao Nakamura
Издательство: IOP Publishing
Год: 2022
Формат: PDF
Страниц: 155
Размер: 12.21 MB
Язык: English

Even today, quantum computing is still at a developing stage. The feature of this book is from introduction to deeply investigated research results. Therefore, the contents are fully satisfied with the authors' earnest will to transfer their concepts to the readers.

Название: Quantum Computing for Programmers
Автор: Robert Hundt
Издательство: Cambridge University Press
Год: 2022
Формат: PDF
Страниц: 375
Размер: 10,54 MB
Язык: English

This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike. Over 25 fundamental algorithms are explained with full mathematical derivations and classical code for simulation, using an open-source code base developed from the ground up in Python and C++.

Название: Foundations of Linux Debugging, Disassembling, and Reversing: Analyze Binary Code, Understand Stack Memory Usage, and Reconstruct C/C++ Code with Intel x64
Автор: Dmitry Vostokov
Издательство: Apress
Год: 2023
Страниц: 181
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.1 MB

Review topics ranging from Intel x64 assembly language instructions and writing programs in assembly language, to pointers, live debugging, and static binary analysis of compiled C and C++ code. This book is ideal for Linux desktop and cloud developers. Using the latest version of Debian, you’ll focus on the foundations of the diagnostics of core memory dumps, live and postmortem debugging of Linux applications, services, and systems, memory forensics, malware, and vulnerability analysis. This requires an understanding of x64 Intel assembly language and how C and C++ compilers generate code, including memory layout and pointers. This book provides the background knowledge and practical foundations you’ll need in order to master internal Linux program structure and behavior. It consists of practical step-by-step exercises of increasing complexity with explanations and ample diagrams. You’ll also work with the GDB debugger and use it for disassembly and reversing. By the end of the book, you will have a solid understanding of how Linux C and C++ compilers generate binary code. In addition, you will be able to analyze such code confidently, understand stack memory usage, and reconstruct original C/C++ code. Foundations of Linux Debugging, Disassembling, and Reversing is the perfect companion to Foundations of ARM64 Linux Debugging, Disassembling, and Reversing for readers interested in the cloud or cybersecurity.

Название: Beginning C++ Compilers: An Introductory Guide to Microsoft C/C++ and MinGW Compilers
Автор: Berik I. Tuleuov, Ademi B. Ospanova
Издательство: Apress
Год: 2024
Страниц: 219
Язык: английский
Формат: pdf (true), epub (true)
Размер: 22.6 MB

This book focuses on how to install C/C++ compilers on Linux and Windows platforms in a timely and efficient way. Installing C/C++ compilers, especially Microsoft compilers, typically takes quite a lot of time because it comes with Microsoft Visual Studio for the vast majority of users. Installing Visual Studio requires usually about 40 GB of disk space and a large amount of RAM, so it is impossible to use weak hardware. The authors provide an easy way to deploy Microsoft C/C++ compiler: with no disk space headache and hardware resources lack. The method described saves significant time since software can even be deployed on removable devices, such as flash sticks, in an easy and portable way. It is achieved by using Enterprise Windows Driver Kit (EWDK), single big ISO image, which can be mounted as virtual device and used directly without any installation. EWDK contains everything from Visual Studio except IDE. EWDK also allows to use MASM64 (Microsoft Macro-Assembly) and C# compilers. With the aid of the MSBuild System, one can compile Visual Studio Projects (.vcxproj) and Solutions (.sln) without even using Visual Studio! Similarly, MinGW compilers can be deployed from 7z/zip archives, simply by unpacking into appropriate location. Both Microsoft C/C++ and MinGW compilers can be used as portable software?an approach that does not require administrative privileges at all. For reader of all skills who wants to save time and efforts to start to work with C++.

Название: Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch, 2nd Edition
Автор: Suman Kalyan Adari, Sridhar Alla
Издательство: Apress
Год: 2024
Страниц: 538
Язык: английский
Формат: pdf (true), epub (true)
Размер: 51.1 MB

This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge Machine Learning and Deep Learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in Deep Learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core Data Science and Machine Learning modeling concepts before delving into traditional Machine Learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using Scikit-learn. Following this, the authors explain the essentials of Machine Learning and Deep Learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to Scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own Machine Learning- or Deep Learning-based anomaly detectors. For data scientists and Machine Learning engineers of all levels of experience interested in learning the basics of Deep Learning applications in anomaly detection.

Название: Python в нескучных примерах (50)
Автор: Алекс Ерофеев
Издательство: stepik
Год: 2023
Формат: Docx
Страниц: много
Размер: 147 Mb
Язык: Русский

Для того чтобы научится программировать - нужно много программировать, совершать ошибки, править код, узнавать как можно сделать код лучше, делать код лучше, и продолжать программировать. В этом курсе нет воды, только большое количество примеров на основании которых вы можете написать много своего кода и стать лучше.

Название: Ultimate Enterprise Data Analysis and Forecasting using Python: Leverage Cloud platforms with Azure Time Series Insights and AWS Forecast Components for Time Series Analysis and Forecasting with Deep learning Modeling using Python
Автор: Shanthababu Pandian
Издательство: Orange Education Pvt Ltd, AVA
Год: December 2023
Страниц: 503
Язык: английский
Формат: epub (true)
Размер: 18.3 MB

Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making. This book covers various aspects of Time Series Analysis and Forecasting using the Python language, emphasizing the importance of time series analysis from an industry perspective for in-depth analysis and forecasting, with real-time use cases and required examples. The primary objective of this book is to provide a detailed pack of time series analysis and forecasting methods, essential in the current digital market, and grow business opportunities using various techniques from an AIML perspective. This book aims to connect the Time Series and Forecasting problem statements across multiple industries and demonstrate how to provide solutions using currently available tools, technology, and evidence of success stories. This book promises that by the end of the reading, the readers will understand time series and forecasting techniques, and also learn how to analyze, design, and maintain the solutions. In this manner, readers can follow the correct path to take the time series components, work on them with Python packages, and understand the data for analysis and productive solutions, such as predicting or forecasting. This book covers the expectations of Data Analysts, Data Scientists, and Machine Learning Engineers who will be involved in time series analysis and forecasting-related projects. This book helps those interested in time series analysis. The book begins with an introduction to Python and its essential packages. It then delves into various aspects of time series data analysis and models from both traditional and ML methods, followed by their implementation in the cloud environment.

Название: Deep Learning for Engineers
Автор: Tariq M. Arif, Md Adilur Rahim
Издательство: CRC Press
Год: 2024
Страниц: 170
Язык: английский
Формат: pdf (true)
Размер: 18.9 MB

Deep Learning for Engineers introduces the fundamental principles of Deep Learning along with an explanation of the basic elements required for understanding and applying Deep Learning models. As a comprehensive guideline for applying Deep Learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed. Some basic knowledge of Python programming is required to follow this book. However, no chapter is devoted to teaching Python programming. Instead, we demonstrated relevant Python commands followed by brief descriptions throughout this book. A common roadblock to exploring the deep learning field by engineering students, researchers, or non-data science professionals is the variation of probabilistic theories and the notations used in Data Science or Computer Science books. In order to avoid this complexity, in this book, we mainly focus on the practical implementation part of deep learning theory using Python programming. This book includes exercise problems for all case studies focusing on various fine-tuning approaches in Deep Learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.

Название: Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner
Автор: Dothang Truong
Издательство: CRC Press
Год: 2024
Страниц: 590
Язык: английский
Формат: pdf (true)
Размер: 35.9 MB

As data continues to grow exponentially, knowledge of Data Science and Machine Learning has become more crucial than ever. Machine Learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize Machine Learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. The book begins with Part I, introducing the core concepts of data science, data mining, and Machine Learning. My aim is to present these principles without overwhelming readers with complex math, empowering them to comprehend the underlying mechanisms of various algorithms and models. This foundational knowledge will enable readers to make informed choices when selecting the right tool for specific problems. In Part II, I focus on the most popular Machine Learning algorithms, including regression methods, decision trees, neural networks, ensemble modeling, principal component analysis, and cluster analysis.

Название: Geographic Data Science with Python
Автор: Sergio Rey, Dani Arribas-Bel, Levi John Wolf
Издательство: CRC Press
Год: 2023
Страниц: 411
Язык: английский
Формат: pdf (true)
Размер: 27.5 MB

This book provides the tools, the methods, and the theory to meet the challenges of contemporary Data Science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts. Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.