Название: jаvascript Native Interface: Build cross-platform apps with native performance
Автор: Tony Gaitatzis
Издательство: BackupBrain
Год: 2024
Страниц: 194
Язык: английский
Формат: pdf, epub
Размер: 10.9 MB

In mobile development, there is a trade-off between portability code and fast execution. A new feature of React Native called jаvascript Native Interface (JSI) removes that trade-off by allowing developers to write portable code that executes as binary code on the native device. jаvascript Interface (JSI) is a lightweight, general-purpose API provided by Facebook for interacting with jаvascript data types and functions in C++ code. Introduced in the context of React Native, JSI offers a way to call C++ functions directly from jаvascript and vice versa. Before JSI, React Native used a “Native Bridge” feature, which required that developers write native code in the device-native language, for example Swift on iOS and Java or Kotlin on Android. React Native developers who wanted to have the performance of a native app therefore needed to maintain two code-bases for the same features. JSI solves this by allowing developers to maintain high-performance code in only one language: C++. While JSI brings numerous advantages, especially for complex and performance-critical applications, it's worth noting that it requires a good understanding of both C++ and jаvascript, as well as the internals of React Native. As such, it's often used for building advanced features, or optimizing performance-critical paths. This book is intended for programmers, engineers, and technology enthusiasts who want to learn about mobile development.

Название: Artificial Intelligence for Risk Mitigation in the Financial Industry
Автор: Ambrish Kumar Mishra, Shweta Anand, Narayan C. Debnath
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 384
Язык: английский
Формат: epub (true)
Размер: 10.8 MB

This book extensively explores the implementation of Artificial Intelligence (AI) in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to "transform inputs into output." As Artificial Intelligence systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Artificial Intelligence (AI) can enhance the efficiency and effectiveness of risk management in the financial sector using Deep Learning, Machine Learning algorithms, and Natural Language Processing (NLP). These methods can be used to ascertain the existence of potential threats, unearth fraudulent activities, and provide predictive analytics that are helpful in making decisions. The application of Artificial Intelligence to risk management has the potential to significantly improve decision-making and to reduce risks and raise overall financial stability.

Название: Fundamentals Of Python: First Programs (MindTap Course List), 3rd Edition
Автор: Kenneth A. Lambert
Издательство: Cengage Learning
Год: 2024
Страниц: 482
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Master today's required Computer Science topics while preparing for further study with Lambert's FUNDAMENTALS OF PYTHON: FIRST PROGRAMS. This book's easygoing approach is ideal, no matter what your background. The approach starts with simple algorithmic code and then scales into working with functions, objects, and classes as the problems become more complex and require new abstraction mechanisms. Rather than working only with numeric or text-based applications like other introductory texts, this edition presents graphics, image manipulation, GUIs, and simple networked client/server applications. The author uses Python's standard Turtle graphics module to introduce graphics and to provide open source frameworks for easy image processing and GUI application development. This book uses the Python programming language as a way of making the first year of studying Computer Science more manageable and attractive for students and instructors alike.

Название: Industrial Recommender System: Principles, Technologies and Enterprise Applications
Автор: Lantao Hu, Yueting Li, Guangfan Cui, Kexin Yi
Издательство: Springer/Publishing House of Electronics Industry
Год: 2024
Страниц: 256
Язык: английский
Формат: pdf (true), epub
Размер: 54.0 MB

Recommender systems, as a highly popular Artificial Intelligence (AI) technology in recent years, have been widely applied across various industries. They have transformed the way we interact with technology, influencing our choices and shaping our experiences. This book provides a comprehensive introduction to industrial recommender systems, starting with the overview of the technical framework, gradually delving into each core module such as content understanding, user profiling, recall, ranking, re-ranking and so on, and introducing the key technologies and practices in enterprises. The book also addresses common challenges in recommendation cold start, recommendation bias and debiasing. Additionally, it introduces advanced technologies in the field, such as Reinforcement Learning, causal inference. Professionals working in the fields of recommender systems, computational advertising, and search will find this book valuable. It is also suitable for undergraduate, graduate, and doctoral students majoring in Artificial Intelligence, Computer Science, software engineering, and related disciplines. Furthermore, it caters to readers with an interest in recommender systems, providing them with an understanding of the foundational framework, insights into core technologies, and advancements in industrial recommender systems.

Название: Large Language Models in Cybersecurity: Threats, Exposure and Mitigation
Автор: Andrei Kucharavy, Octave Plancherel, Valentin Mulder, Alain Mermoud
Издательство: Springer
Год: 2024
Страниц: 249
Язык: английский
Формат: pdf (true)
Размер: 11.3 MB

This book provides cybersecurity practitioners with the knowledge needed to understand the risks of the increased availability of powerful large language models (LLMs) and how they can be mitigated. It attempts to outrun the malicious attackers by anticipating what they could do. It also alerts LLM developers to understand their work's risks for cybersecurity and provides them with tools to mitigate those risks. The book starts in Part I with a general introduction to LLMs and their main application areas. Part II collects a description of the most salient threats LLMs represent in cybersecurity, be they as tools for cybercriminals or as novel attack surfaces if integrated into existing software. Part III focuses on attempting to forecast the exposure and the development of technologies and science underpinning LLMs, as well as macro levers available to regulators to further cybersecurity in the age of LLMs. Eventually, in Part IV, mitigation techniques that should allow safe and secure development and deployment of LLMs are presented. The book concludes with two final chapters in Part V, one speculating what a secure design and integration of LLMs from first principles would look like and the other presenting a summary of the duality of LLMs in cyber-security. While the general public discovered Large Language Models (LLMs) with ChatGPT—a generative autoregressive model, they are far from the only models in the LLM family. Various architectures and training regiments optimized for specific usages were designed throughout their development, which were then classified as different LLM families.

Название: Ultimate Pentesting for Web Applications: Unlock Advanced Web App Security Through Penetration Testing Using Burp Suite, Zap Proxy, Fiddler, Charles Proxy, and Python for Robust Defense
Автор: Rohit Gautam, Shifa Cyclewala
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 747
Язык: английский
Формат: pdf, epub
Размер: 17.3 MB

Discover the essential tools and insights to safeguard your digital assets with the "Ultimate Pentesting for Web Applications". This essential resource comprehensively covers ethical hacking fundamentals to advanced testing methodologies, making it a one-stop resource for web application security knowledge. Delve into the intricacies of security testing in web applications, exploring powerful tools like Burp Suite, ZAP Proxy, Fiddler, and Charles Proxy. Real-world case studies dissect recent security breaches, offering practical insights into identifying vulnerabilities and fortifying web applications against attacks. This handbook provides step-by-step tutorials, insightful discussions, and actionable advice, serving as a trusted companion for individuals engaged in web application security. Each chapter covers vital topics, from creating ethical hacking environments to incorporating proxy tools into web browsers. It offers essential knowledge and practical skills to navigate the intricate cybersecurity landscape confidently. By the end of this book, you will gain the expertise to identify, prevent, and address cyber threats, bolstering the resilience of web applications in the modern digital era. Using scripting languages like Python or Bash, security professionals can create custom scripts that mimic the behavior of web crawlers but with added intelligence. These scripts can adapt to unique scenarios and uncover hidden content more effectively.

Название: Продвинутый python: заряди свои батарейки
Автор: Артем Егоров
Издательство: Stepik
Год: 2024
Формат: HTML
Страниц: много
Размер: 420 Mb
Язык: Русский

Данный курс позволит вам познакомиться со всеми основными модулями стандартной библиотеке python (за исключением модулей asyncio, multiprocessing, threading) Вы получите массу новых знаний, решите уйму задач на программирование, причем задания будут как по написанию функций, так и созданию классов, что требует знание ООП. Курс позволит выйти вам на новый уровень. Особенность его заключается в практической направленности: практически в каждом разделе мы будем применять полученные знания и вы научитесь работать с файловой системой, отправлять письма, научитесь логированию и многому другому, что будет полезно для вас.

Название: Embedding Artificial Intelligence into ERP Software: A Conceptual View on Business AI with Examples from SAP S/4HANA
Автор: Siar Sarferaz
Издательство: Springer
Год: 2024
Страниц: 436
Язык: английский
Формат: pdf (true), epub
Размер: 57.5 MB

This book explains how to embed Artificial Intelligence (AI) in digitized business processes of ERP software by solving the two related substantial challenges: how can Artificial Intelligence be systematically integrated into ERP business processes for ease of consumption, and how can Artificial Intelligence be made enterprise-ready by covering ERP qualities like compliance, lifecycle management, extensibility, or scalability? As a general introduction, the first part of this book takes the reader through a historical journey towards intelligent ERP systems. In addition, reference processes and a reference architecture for ERP systems are proposed which build the foundation for the suggested subsequent solution concept, including a method for operationalizing intelligence for ERP business processes. Subsequently, in the second part detailed concepts of embedding Artificial Intelligence into ERP software are proposed. In this context the suggested solution architecture is depicted, and specific topics are resolved like data integration, model validation, explainability, data protection and privacy, model degradation and performance. In the last part an implementation framework is suggested which enables the previously introduced concepts and harmonizes the development and operations of Artificial Intelligence ERP applications. This part concludes with case studies considering Artificial Intelligence scenarios of SAP S/4HANA in the areas of logistics, finance and sales which apply the defined solution approach and shows its real-world feasibility. In the context of ERP systems, Artificial Intelligence is utilized to imbue business processes with intelligence, increasing the automation and optimization levels. Data Science techniques are applied for this purpose.

Название: Predictive Analytics for the Modern Enterprise: A Practitioner's Guide to Designing and Implementing Solutions (Final Release)
Автор: Nooruddin Abbas Ali
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 361
Язык: английский
Формат: True PDF/True EPUB (Retail Copy)
Размер: 32.6 MB

The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Pandas is an open source Python package that makes it easy to perform data wrangling in Python. It is built on top of the NumPy library that we discussed in the previous section. Pandas makes it easy to work with data for the purpose of Data Science and data analytics. Just as for NumPy the main object provided by the library was an n-dimensional array, for Pandas the main object is a Pandas DataFrame. TensorFlow was created and made open source by Google. It is one of the most-used platforms worldwide for Machine Learning (ML). TensorFlow has wide-ranging applications, including structured data processing, automated image classification, advanced optical character recognition (OCR), video analysis, and sentiment analysis, to name a few. The primary audience for this book is data professionals and technical managers who want to learn the scientific foundation for predictive analytics and its application in today’s enterprise. The book will allow readers to navigate the life cycle of developing predictive analytics capabilities using multiple frameworks, techniques, and platforms. I assume no prior knowledge on the part of the reader except for a basic understanding of programming in Python, a high-level understanding of foundational mathematics, and a high-level understanding of Amazon Web Services (AWS).

Название: Recent Advances in Logo Detection Using Machine Learning Paradigms: Theory and Practice
Автор: Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain
Издательство: Springer
Год: 2024
Страниц: 128
Язык: английский
Формат: pdf (true), epub
Размер: 32.9 MB

This book presents the current trends in Deep Learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using Deep Learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues. In recent years, there has been a notable transition from rule-based Artificial Intelligence (AI) to Machine-Learning-based AI, with an increasing focus on Deep Learning. Currently, Deep Learning frameworks are at the forefront of AI technology, marking significant advancements in academic and industrial fields. The evolution of deep convolutional neural networks (CNNs) has positioned deep learning as the method of choice for computer vision tasks, including image classification, segmentation, object detection, and human keypoint detection. This surge in Deep Learning has sparked interest in applying Convolutional Neural Networks (CNNs) across various domains, including the task of logo detection. The book is directed to professors, researchers, practitioners in the field of engineering, Computer Science, and related fields as well as anyone interested in using Deep Learning techniques and applications in logo and various object detection tasks.