Название: Quantum Machine Learning: Quantum Algorithms and Neural Networks
Автор: Pethuru Raj, Houbing Herbert Song, Dac-Nhuong Le, Narayan Vyas
Издательство: De Gruyter
Год: 2024
Страниц: 336
Язык: английский
Формат: pdf (true), epub
Размер: 34.5 MB

Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of Machine Learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more. Quantum computing (QC) can process calculations tenfold quicker than traditional computing by utilizing the unique characteristics of quantum bits or qubits. The three fundamental ideas of quantum mechanics – superposition, entanglement, and interference – give QC its particular strength. A qubit can store quantum information in a state of superposition, which combines all of the qubit’s potential configurations. Computational spaces can be made complex and multidimensional by using multiple qubits in superposition. These spaces allow for many representations of complex problems.

Название: Machine Learning Algorithms in Depth (Final Release)
Автор: Vadim Smolyakov
Издательство: Manning Publications
Год: 2024
Страниц: 328
Язык: английский
Формат: pdf (true)
Размер: 26.6 MB

Learn how Machine Learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
Fully understanding how Machine Learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you'll explore practical implementations of dozens of ML algorithms. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting Machine Learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you'll learn the fundamentals of Bayesian inference and Deep Learning. You'll also explore the core data structures and algorithmic paradigms for Machine Learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they're put into action. Learn how Machine Learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You'll especially appreciate author Vadim Smolyakov's clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. This book was written for anyone interested in exploring Machine Learning algorithms in depth. The prerequisites for reading this book include a basic level of programming skills in Python, and an intermediate level of understanding of linear algebra, applied probability, and multivariable calculus.

Название: The Complete Obsolete Guide to Generative AI (Final Release)
Автор: David Clinton
Издательство: Manning Publications
Год: 2024
Страниц: 240
Язык: английский
Формат: pdf (true)
Размер: 22.9 MB

The last book on AI you'll ever need. We swear! AI technology moves so fast that this book is probably already out of date! But don't worry—The Complete Obsolete Guide to Generative AI is still an essential read for anyone who wants to make generative AI into a tool rather than a toy. It shows you how to get the best out of AI no matter what changes come in the future. You'll be able to use common automation and scripting tools to take AI to a new level, and access raw (and powerful) GPT models via API. Where to get started? How about creating exciting images, video, and even audio with AI. Need more? Learn to harness AI to speed up any everyday work task, including writing boilerplate code, creating specialized documents, and analyzing your own data. Push beyond simple ChatGPT prompts! Discover ways to double your productivity and take on projects you never thought were possible! AI—and this book—are here to show you how. Everything you learn about Generative AI tools like Chat-GPT, Copilot, and Claude becomes obsolete almost immediately. So how do you decide where to spend your time—and your company's money? This entertaining and unbelievably practical book shows you what you can (and should!) do with AI now and how to roll with the changes as they happen. I’ll also show you how to use code to automate your prompts. Most of what we’ll see will be useful even if you don’t use the Python and Bash code I’ll show you. Written for developers, admins, and other IT pros. Some examples use simple Python code.

Название: High Performance Python: Practical Performant Programming for Humans, 3rd Edition (Early Release)
Автор: Micha Gorelick, Ian Oszvald
Издательство: O’Reilly Media, Inc.
Год: 2024-07-26
Страниц: 226
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Your Python code may run correctly, but what if you need it to run faster? This practical book shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By explaining the fundamental theory behind design choices, this expanded edition of High Performance Python helps experienced Python programmers gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for social media analytics, productionized Machine Learning, and more.

Название: Sustainable Development Using Private AI: Security Models and Applications
Автор: Uma Maheswari V, Rajanikanth Aluvalu
Издательство: CRC Press
Год: 2025
Страниц: 319
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book covers the fundamental concepts of Private AI and its applications. It also covers fusion of Private AI with cutting-edge technologies like cloud computing, Federated Learning and computer vision. Security Models and Applications for Sustainable Development Using Private AI reviews various encryption algorithms used for providing security in Private AI. It discusses the role of training Machine Learning and Deep learning technologies in Private AI. The book provides case studies of using Private AI in various application areas such as purchasing, education, entertainment, medical diagnosis, predictive care, conversational personal assistants, wellness apps, early disease detection, and recommendation systems. The authors provide additional knowledge to handling the customer’s data securely and efficiently. It also provides multi-model dataset storage approaches along with the traditional approaches like anonymization of data and differential privacy mechanisms. Private AI, also known as privacy-preserving AI or confdential AI, is a subset of Artifcial Intelligence that focuses on developing techniques and technologies to protect the privacy and confdentiality of data and models used in AI applications. Privacy is a critical concern in the feld of AI because many AI systems require access to sensitive and personal data, which, if mishandled, can lead to privacy breaches and other adverse consequences. The target audience includes undergraduate and postgraduate students in Computer Science, Information technology, Electronics and Communication Engineering and related disciplines.

Название: React in Depth (Final Release)
Автор: Morten Barklund
Издательство: Manning Publications
Год: 2024
Страниц: 434
Язык: английский
Формат: pdf (true)
Размер: 23.3 MB

A guide to the advanced React skills used by the very best React developers. React in Depth teaches the React libraries, tools and techniques that are vital to build amazing apps. You’ll put each skill you learn into practice with hands-on projects like a goal-focused task manager, expenses tracker, and custom UI library. React in Depth focuses on the modern best practices of React development, with full and up-to-date coverage of the latest features and changes to the React ecosystem. This book highlights the advanced techniques that turn a React pro into a React wizard, and how you can future-proof your career by mastering new React technologies as they emerge. React in Depth teaches you the best practices of React development, with up-to-date coverage of the React ecosystem. In it, you’ll learn how to put NextJS, Remix, TypeScript, and more in your React toolbox. You’ll explore advanced topics like component patterns, optimization techniques, and developer tooling. Each skill is proven with hands-on examples, from a weather app to a Wordle clone. This book is designed for web developers of all kinds. Whether you’re working on the frontend or the backend or as a fullstack developer, if you’re looking to deepen your expertise in creating both interactive web applications and static sites with modern generators, this book is for you. Ideal readers are already familiar with the fundamentals of React, including JSX, functional components, state management, event handling, and form processing. A solid grasp of HTML, CSS, jаvascript, command-line tools, Git, GitHub, npm, and browser developer tools is also strongly encouraged.

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.