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: 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.

Название: Image Processing and Machine Learning, Volume 1: Foundations of Image Processing
Автор: Erik Cuevas, Alma Nayeli Rodríguez
Издательство: CRC Press
Год: 2024
Страниц: 225
Язык: английский
Формат: pdf (true)
Размер: 40.9 MB

Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. Our primary objective was to create a comprehensive textbook that serves as an invaluable resource for an image processing class. With this goal in mind, we carefully crafted a book that encompasses both the theoretical foundations and practical applications of the most prevalent image processing methods. From pixel operations to geometric transformations, spatial filtering to image segmentation, and edge detection to color image processing, we have meticulously covered a wide range of topics essential to understanding and working with images. Moreover, recognizing the increasing relevance of ML in image processing, we have incorporated fundamental ML concepts and their applications in this field. By introducing readers to these concepts, we aim to equip them with the necessary knowledge to leverage ML techniques for various image processing tasks. Volume 1 is organized in a way that allows readers to easily understand the goal of each chapter and reinforce their understanding through practical exercises using MATLAB programs.

Название: Image Processing and Machine Learning, Volume 2: Advanced Topics in Image Analysis and Machine Learning
Автор: Erik Cuevas, Alma Nayeli Rodríguez
Издательство: CRC Press
Год: 2024
Страниц: 239
Язык: английский
Формат: pdf (true)
Размер: 31.6 MB

Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important Machine Learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1. Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data and make informed predictions or decisions without the need for explicit programming. ML finds extensive applications in various domains. For instance, in automation, ML algorithms can automate tasks that would otherwise rely on human intervention, thereby reducing errors and enhancing overall efficiency. Predictive analytics is another area where ML plays a crucial role. By analyzing vast datasets, ML models can detect patterns and make predictions, facilitating applications such as stock market analysis, fraud detection, and customer behavior analysis. We have observed that students grasp the material more effectively when they have access to code that they can manipulate and experiment with. In line with this, our book utilizes MATLAB as the programming language for implementing the systems.

Название: Расширенная аналитика с PySpark: Практические примеры анализа больших наборов данных с использованием Python и Spark
Автор: Акаш Тандон, Сэнди Райза, Ури Ласерсон
Издательство: БХВ-Петербург
Год: 2023
Страниц: 226
Язык: русский
Формат: pdf, djvu
Размер: 36.3 MB

Книга посвящена практическим методам анализа больших объемов данных с использованием языка Python и фреймворка Spark, она знакомит с моделью программирования Spark и основами системы с открытым исходным кодом PySpark. Каждая глава описывает отдельный аспект анализа данных, показаны основы обработки данных в PySpark и Python на примере очистки данных, подробно освещается машинное обучение с помощью Spark. Книга поможет читателю понять, как устроен и работает весь конвейер PySpark для комплексной аналитики больших наборов данных: от создания и оценки моделей до очистки, предварительной обработки и исследования данных с особым акцентом на производственные приложения. Отдельные главы посвящены обработке изображений и библиотеке Spark NLP. Эта книга не рассказывает о достоинствах и недостатках PySpark. Книга знакомит с моделью программирования Spark и основами PySpark — API Python для Spark. Тем не менее она не претендует на то, чтобы служить справочником по Spark или быть исчерпывающим путеводителем по всем закоулкам Spark. Она также не претендует на роль справочника по машинному обучению, статистике или линейной алгебре, хотя во многих главах содержится небольшой вводный материал перед их использованием. Эта книга поможет читателю понять, как устроен и работает весь конвейер PySpark для комплексной аналитики больших наборов данных, а это не только создание и оценка моделей, но также очистка, предварительная обработка и исследование данных с особым акцентом на производственные приложения.

Название: Learn Programming with C An Easy Step-by-Step Self-Practice Book for Learning C
Автор: Sazzad M.S. Imran, Atiqur Rahman Ahad
Издательство: CRC Press
Год: 2024
Страниц: 580
Язык: английский
Формат: pdf (true)
Размер: 22.6 MB

Authored by two standout professors in the field of Computer Science and Technology with extensive experience in instructing, Learn Programming with C: An Easy Step-by Step Self-Practice Book for Learning C is a comprehensive and accessible guide to programming with one of the most popular languages. Meticulously illustrated with figures and examples, this book is a comprehensive guide to writing, editing, and executing C programs on different operating systems and platforms, as well as how to embed C programs into other applications and how to create one’s own library. A variety of questions and exercises are included in each chapter to test the readers’ knowledge. Written for the novice C programmer, especially undergraduate and graduate students, this book’s line-by-line explanation of code and succinct writing style makes it an excellent companion for classroom teaching, learning, and programming labs. C is a programming language with which every software developer should become familiar. Though numerous books are available on C programming language, most of the example programs are written without algorithms or any flowchart in those books. As a result, it becomes difficult for a student to comprehend the core of a programming language through a self-learning approach. Our experience in teaching C underscores the importance of presenting C programs by the flowchart solution first, then the pseudocode solution, and finally the actual C code with the line-by-line explanation. It is written for C programming language courses/modules at the undergraduate and graduate levels - mostly for beginners.