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Название: Data Science and Risk Analytics in Finance and Insurance
Автор: Tze Leung Lai, Haipeng Xing
Издательство: CRC Press
Год: 2025
Страниц: 464
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book presents statistics and Data Science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of Machine Learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: Artificial Intelligence, blockchain, cloud computing, and big data analytics. In general, machine learning methods are broadly categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. This chapter provides an overview of some commonly used supervised and unsupervised methods and explores their applications in finance. The subsequent chapter presents Markov decision processes and widely used reinforcement learning techniques, further expanding the understanding of machine learning’s role in optimizing decision-making processes within the financial domain.

Название: AI for Everyone: A Beginner's Handbook for Artificial Intelligence
Автор: Saptarsi Goswami, Amit Kumar Das, Amlan Chakrabarti
Издательство: Pearson
Год: 2024
Страниц: 192
Язык: английский
Формат: epub
Размер: 10.1 MB

“AI for Everyone" is a humble attempt made by authors to introduce the basic concepts of Artificial Intelligence or AI in a simple but comprehensive way. The book starts with a quick anecdote of the evolution of AI, which is followed by presenting the industry use cases across diverse domains. It also discusses the different technology areas under the gamut of AI, the ethical concerns related to AI and how they should be addressed, and research opportunities related to AI. It ends with a discussion of the emerging trends and future directions in AI. The authors, being veteran professionals in the areas of academics and industry, have tried to bring in comprehensively all the required elements of knowledge - both in the areas of academic and industry practitioner. Readers of this book will gain a basic understanding of AI concepts. Not only students, but also the general readers will find a variety of concepts related to AI. Students, software developers, technical managers as well as general readers with no background in Computer Science or programming will find the material in this book easily readable.

Название: Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models
Автор: Leonidas Deligiannidis, George Dimitoglou, Hamid R. Arabnia
Издательство: De Gruyter
Год: 2024
Страниц: 442
Язык: английский
Формат: pdf (true), epub
Размер: 27.5 MB

Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of Computer Science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges. From the Computer Science perspective, the core of Artificial Intelligence (AI) includes Machine Learning. In recent years the growth in utilizing AI applications has been exponential. One reason for this exponential growth has been the advancement in Machine Learning; many give credit for this advancement to Deep Learning (via Convolution Neural Networks, CNN) and new applications in Large Language Models (LLMs). This book covers the emerging trends in AI, Machine Learning, CNNs, and LLMs. Machine Learning methods heavily rely on large datasets. Although the topic of Data Science is not explicitly addressed in this book, many algorithms and methodologies that appear in this book utilize Data Science methodologies.

Название: Writing a C Compiler: Build a Real Programming Language from Scratch (Final)
Автор: Nora Sandler
Издательство: No Starch Press
Год: 2024
Страниц: 904
Язык: английский
Формат: True/Retail (PDF EPUB MOBI)
Размер: 57.6 MB

Compilers are at the heart of everything programmers do, yet even experienced developers find them intimidating. For those eager to truly grasp how compilers work, Writing a C Compiler dispels the mystery. This book guides you through a fun and engaging project where you’ll learn what it takes to compile a real-world programming language to actual assembly code. Writing a C Compiler will take you step by step through the process of building your own compiler for a significant subset of C—no prior experience with compiler construction or assembly code needed. Once you’ve built a working compiler for the simplest C program, you’ll add new features chapter by chapter. The algorithms in the book are all in pseudocode, so you can implement your compiler in whatever language you like. Compilers aren’t terrifying beasts—and with help from this hands-on, accessible guide, you might even turn them into your friends for life. I wrote this book for programmers who are curious about how compilers work. Many books about compiler construction are written as textbooks for college or graduate-level classes, but this one is meant to be accessible to someone exploring the topic on their own. You won’t need any prior knowledge of compilers, interpreters, or assembly code to complete this project. A basic understanding of computer architecture is helpful, but not essential; I’ll discuss important concepts as they come up and occasionally point you to outside resources with more background information. That said, this is not a book for novice programmers. You should be comfortable writing substantive programs on your own, and you should be familiar with binary numbers, regular expressions, and basic data structures like graphs and trees. You’ll need to know C well enough to read and understand small C programs, but you don’t need to be an expert C programmer. We’ll explore the ins and outs of the language as we go.

Название: Black Hat Bash: Creative Scripting for Hackers and Pentesters (Final)
Автор: Dolev Farhi, Nick Aleks
Издательство: No Starch Press
Год: 2025
Страниц: 347
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Master the art of offensive bash scripting. This highly practical hands-on guide covers chaining commands together, automating tasks, crafting living-off-the-land attacks, and more! In the hands of the penetration tester, bash scripting becomes a powerful offensive security tool. InBlack Hat Bash, you’ll learn how to use bash to automate tasks, develop custom tools, uncover vulnerabilities, and execute advanced, living-off-the-land attacks against Linux servers. You’ll build a toolbox of bash scripts that will save you hours of manual work. And your only prerequisite is basic familiarity with the Linux operating system. You’ll learn the basics of bash syntax, then set up a Kali Linux lab to apply your skills across each stage of a penetration test—from initial access to data exfiltration. Along the way, you’ll learn how to perform OS command injection, access remote machines, gather information stealthily, and navigate restricted networks to find the crown jewels. Hands-on exercises throughout will have you applying your newfound skills. Whether you’re a pentester, a bug bounty hunter, or a student entering the cybersecurity field, Black Hat Bash will teach you how to automate, customize, and optimize your offensive security strategies quickly and efficiently, with no true sorcery required.

Название: Python Coding for Beginners - 19th Edition, 2024
Автор: Papercut Limited
Издательство: Papercut Limited
Год: 2024
Язык: английский
Формат: pdf
Размер: 44.9 MB

"Программирование на Python для начинающих" - это первый и единственный выбор, если вы начинающий и хотите узнать все, что вам нужно, чтобы начать программировать. Это независимое руководство наполнено полезными рекомендациями и пошаговыми примерами с цветными иллюстрациями, написанными на простом и понятном английском языке. На страницах этого нового руководства вы узнаете все, что вам нужно знать о написании своих собственных потрясающих приложений. С этим неофициальным руководством по эксплуатации ни одна проблема не будет неразрешимой, ни один вопрос не останется без ответа, пока вы учитесь, изучаете и улучшаете свой пользовательский опыт.

Название: Data Science Fundamentals with R, Python, and Open Data
Автор: Marco Cremonini
Издательство: Wiley
Год: 2024
Страниц: 480
Язык: английский
Формат: pdf (true), epub (true)
Размер: 12.8 MB

Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start Data Science projects. Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out Data Science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers’ active learning. Each chapter presents one or more case studies. Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to Computer Science, and medical fields using stochastic and quantitative models.

Название: Bio-Inspired Optimization for Medical Data Mining
Автор: Sumit Srivastava, Abhineet Anand, Abhishek Kumar, Bhavna Saini
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 326
Язык: английский
Формат: pdf (true)
Размер: 23.5 MB

This book is a comprehensive exploration of bio-inspired optimization techniques and their potential applications in healthcare. Bio-Inspired Optimization for Medical Data Mining is a groundbreaking book that delves into the convergence of nature’s ingenious algorithms and cutting-edge healthcare technology. Through a comprehensive exploration of state-of-the-art algorithms and practical case studies, readers gain unparalleled insights into optimizing medical data processing, enabling more precise diagnosis, optimizing treatment plans, and ultimately advancing the field of healthcare. Bioinspired algorithms, also known as nature-inspired algorithms or evolutionary computation, are computational techniques that draw inspiration from the principles, behaviors, and mechanisms observed in biological systems. These algorithms mimic the adaptive and problem-­solving abilities found in nature to tackle complex optimization problems. By emulating the evolutionary processes, swarm behaviors, neural networks, or other biological phenomena, bioinspired algorithms offer innovative and efficient problem-solving approaches. Bioinspired algorithms have gained significant importance in various fields due to their ability to handle complex and challenging problems. The application areas of these algorithms are optimization, Machine Learning, robotics, data mining, and pattern recognition.

Название: Deep Learning: A Practical Introduction
Автор: Manel Martínez-Ramón, Meenu Ajith, Aswathy Rajendra Kurup
Издательство: Wiley
Год: 2024
Страниц: 405
Язык: английский
Формат: pdf (true)
Размер: 15.7 MB

An engaging and accessible introduction to Deep Learning perfect for students and professionals. In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of Deep Learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary Deep Learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. In this chapter, we introduce the basic elements of Python to be used throughout the book, and we will revisit the code previously introduced in Chapter 3, among other examples. Perfect for undergraduate and graduate students studying Computer Vision, Computer Science, Artificial Intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of Deep Learning and Machine Learning in general.

Название: Deep Learning with PyTorch, Second Edition (MEAP v5)
Автор: Luca Antiga, Eli Stevens, Howard Huang
Издательство: Manning Publications
Год: 2024
Страниц: 326
Язык: английский
Формат: pdf (true), epub
Размер: 27.0 MB

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models. Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems. This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using Deep Learning and PyTorch.