Название: Neural Networks for Beginners: Comprehensive Guide to Understanding the Power of Artificial Intelligence
Автор: Sam Campbell
Издательство: Independently published
Год: 2023
Язык: английский
Формат: pdf
Размер: 24.9 MB

Dive into the foundations of Artificial Intelligence, demystifying the core concept of Neural Networks. From the basics of neurons and synapses to the intricate architecture of these intelligent systems, each chapter unfolds the secrets behind the machines that can learn, adapt, and make decisions on their own. Learn the building blocks of neural networks, from activation functions to the crucial role of data in training these intelligent systems. Gain insights into popular neural network architectures, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), exploring their applications in image recognition, natural language processing, and more. Practicality meets theory as you discover the tools and frameworks powering neural networks, with an introduction to TensorFlow, PyTorch, and the user-friendly Keras. Explore the world of ethical considerations in AI, delving into issues of bias, fairness, and transparency that accompany the development of intelligent systems. Whether you're a student, enthusiast, or professional seeking to grasp the fundamentals, "Neural Networks Unveiled" empowers you with the knowledge to navigate the evolving landscape of Artificial Intelligence. Embark on your journey into the future—where machines learn, adapt, and innovate alongside us. "Neural Networks Unveiled" is your key to unlocking the mysteries of Artificial Intelligence and embracing the limitless possibilities that lie ahead.

Название: Principles and Applications of Quantum Computing Using Essential Math
Автор: A. Daniel, M. Arvindhan, Kiranmai Bellam, N. Krishnaraj
Издательство: IGI Global
Год: 2023
Страниц: 252
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

In the swiftly evolving realm of technology, the challenge of classical computing's constraints in handling intricate problems has become pronounced. While classical computers excel in many areas, they struggle with complex issues in cryptography, optimization, and molecular simulation. Addressing these escalating challenges requires a disruptive solution to push the boundaries of computation and innovation. Principles and Applications of Quantum Computing Using Essential Math, authored by A. Daniel, M. Arvindhan, Kiranmai Bellam, and N. Krishnaraj. This guide pioneers the transformative potential of quantum computing by seamlessly blending rigorous mathematics with quantum theory. It equips scholars, researchers, and aspiring technologists with insights to grasp and harness quantum computing's capabilities. By delving into quantum gates, algorithms, and error correction techniques, the book demystifies quantum computing, inviting exploration of Quantum Machine Learning, cryptography, and the dynamic interplay between classical and quantum computing. As the quantum landscape expands, this book acts as a vital companion, navigating readers through the converging realms of industry, academia, and innovation. Principles and Applications of Quantum Computing Using Essential Math arrives as a timely answer to the limitations of classical computing, providing scholars with an essential roadmap to navigate the quantum technology landscape. With its clear explanations, practical applications, and forward-looking perspectives, this book serves as an indispensable tool for unraveling quantum computing's mysteries and driving innovation into uncharted domains. Emerging technologies, including quantum information science and artificial education systems, have the potential to have significant implications for the future of human civilization. Quantum information, on the one hand, and Machine Learning (ML) and Artificial Intelligence (AI), on the other, consume their personal unique set of queries and contests that have been studied in isolation up until now. However, a recent study is starting to examine whether these disciplines can teach one another anything useful.

Название: Machine Learning and Optimization for Engineering Design
Автор: Apoorva S. Shastri, Kailash Shaw, Mangal Singh
Издательство: Springer
Год: 2023
Страниц: 175
Язык: английский
Формат: pdf (true), epub
Размер: 35.5 MB

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to Machine Learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, Machine Learning, Artificial Intelligence, modified/newly developed Machine Learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here. There are several deterministic and approximation-based optimization methods that have been developed by the researchers, such as branch-and-bound techniques, simplex methods, approximation and Artificial Intelligence-based methods such as evolutionary methods, Swarm-based methods, physics-based methods, socio-inspired methods, etc. In the paper "OpenCV and MQTT Based Intelligent Management System", a system is proposed which is intelligent and can perform identification, counting, and calculation of density of vehicles. After calculating the traffic density, the system classifies the density into low, medium, and high density with the help of a decision algorithm. This system is based on Python programming, and the libraries used in Python are Open-source Computer Vision, NumPy, Chardet, and time library.

Название: Jumping Computation: Updating Automata and Grammars for Discontinuous Information Processing
Автор: Alexander Meduna, Zbyněk Křivka
Издательство: CRC Press
Год: 2024
Страниц: 294
Язык: английский
Формат: pdf (true)
Размер: 14.5 MB

Jumping Computation: Updating Automata and Grammars for Discontinuous Information Processing is primarily a theoretically oriented treatment of jumping automata and grammars, covering all essential theoretical topics concerning them, including their power, properties, and transformations. From a practical viewpoint, it describes various concepts, methods, algorithms, techniques, case studies and applications based upon these automata and grammars. In today’s computerized world, the scientific development and study of computation, referred to as the theory of computation, plays a crucial role. One important branch, language theory, investigates how to define and study languages and their models, which formalize algorithms according to which their computation is executed. These language-defining models are classified into two basic categories: automata, which define languages by recognizing their words, and grammars, which generate them. Introduced many decades ago, these rules reflect classical sequential computation. However, today’s computational methods frequently process information in a fundamentally different way, frequently “jumping” over large portions of the information as a whole. This book adapts classical models to formalize and study this kind of computation properly. Simply put, during their language-defining process, these adapted versions, called jumping automata and grammars, jump across the words they work on.

Название: Quantum Computing by Practice: Python Programming in the Cloud with Qiskit and IBM-Q, Second Edition
Автор: Vladimir Silva
Издательство: Apress
Год: 2024
Страниц: 412
Язык: английский
Формат: pdf (true), epub (true)
Размер: 17.1 MB

Learn to write algorithms and program in the new field of quantum computing. This second edition is updated to equip you with the latest knowledge and tools needed to be a complex problem-solver in this ever-evolving landscape. The book has expanded its coverage of current and future advancements and investments by IT companies in this emerging technology. Most chapters are thoroughly revised to incorporate the latest updates to IBM Quantum's systems and offerings, such as improved algorithms, integrating hardware advancements, software enhancements, bug fixes, and more. You’ll examine quantum computing in the cloud and run experiments there on a real quantum device. Along the way you’ll cover game theory with the Magic Square, an example of quantum pseudo-telepathy. You’ll also learn to write code using QISKit, Python SDK, and other APIs such as QASM and execute it against simulators (local or remote) or a real quantum computer. Then peek inside the inner workings of the Bell states for entanglement, Grover’s algorithm for linear search, Shor’s algorithm for integer factorization, and other algorithms in the fields of optimization, and more. Finally, you’ll learn the current quantum algorithms for entanglement, random number generation, linear search, integer factorization, and others.

Название: Supervised Machine Learning with Python: A Comprehensive guide to Supervised Learning for 2024
Автор: Hayden Van Der Post
Издательство: Reactive Publishing
Год: 2023
Страниц: 365
Язык: английский
Формат: pdf
Размер: 54.4 MB

Push the boundaries of Machine Learning with Python and elevate your data analysis skills to new heights! 'Supervised Machine Learning with Python' is the essential guide for professionals who have mastered the basics and are ready to dive into the more complex and powerful aspects of machine learning. If you were captivated by the top-selling introductory texts, this book is the perfect next step to satisfy your thirst for advanced knowledge. With clear explanations, practical examples, and expert techniques, this book will help you unlock deeper insights from your data. Inside, you will find advanced strategies for model selection, feature engineering, and tuning that will enable you to achieve superior predictive performance. 'Supervised Machine Learning with Python' is specifically designed for professionals who are ready to use Python's powerful libraries, such as Scikit-learn, Pandas, and NumPy, to deploy models that can learn from data. You'll learn not only the theory behind the algorithms but also how to implement them in practice to solve the kind of data analytical problems you encounter every day. In these pages, you will find advanced case studies that vividly illustrate the applications of supervised Machine Learning in various industries. Whether you work in finance, healthcare, retail, or any other field where data drives decisions, this book is tailored to help you add a new level of sophistication to your work.

Название: Improve Java Coding: Best Practices for Effective Coding
Автор: Sar Maroof
Издательство: Independently published
Год: 2023
Страниц: 452
Язык: английский
Формат: pdf
Размер: 45.0 MB

The journey of improving coding skills continues even after learning programming. The key question becomes: How can one write code professionally while addressing critical concerns such as code maintainability, code re-usability, security issues, and memory leaks? This book focuses on real-world code examples, aiming to avoid unnecessary, boring, extended theoretical discussions. Instead, it goes straight to the point in coding and offers improvements with clear explanations. Any building is eventually finished, but successful software is never truly finished. New features need to be built, causing the code base to grow over time, and concerns about maintainability grow with it if programmers don't pay serious attention to it. If you asked an expert and a junior programmer to write code for a specific program, you would realize that the expert accomplishes the goal with significantly less code than the junior programmer. Every extra line of code written unnecessarily creates more issues in terms of maintainability, opening the doors to hacking and security vulnerabilities. The problem with coding is not that poorly written code doesn't work; the real problem is that it does. Therefore, it can be challenging for an inexperienced programmer to realize what a better version could be. In this book, I will provide you with common mistakes through different pieces of code that function correctly, but I will also explain why it is crucial to avoid that kind of coding, even though it works. Before diving into the chapters, let's highlight a few code issues to help you determine whether this book will benefit you. The code examples provide a preview of this book's approach to assist you in improving your Java coding.

Название: ITS-303 Python Programming Realistic Mock Tests: 340+ Realistic questions and explanations to get you ITS-303 Exam certified on your 1st attempt
Автор: Abound Academy
Издательство: Abound Academy
Год: 2023
Страниц: 455
Язык: английский
Формат: epub
Размер: 38.8 MB

Welcome to Python Programming Practice Tests book, a comprehensive guide filled with practice tests designed to help you prepare for the ITS-303 Python certification exam. The book is intended for individuals who have basic knowledge of Python programming and are seeking to obtain the ITS-303 certification to validate their skills. The ITS-303 certification is a valuable asset in today's job market as it demonstrates your proficiency in Python programming. However, passing the exam can be challenging, and it requires a lot of preparation and practice. That's where this book comes in handy. This book provides you with more than 340 realistic Python questions and detailed explanations to help you prepare for the ITS-303 certification exam. The questions are arranged in chapters that cover all the essential topics in Python programming, including data types, functions, control structures, object-oriented programming, file handling, and more. Each chapter contains multiple choice questions, true/false questions, and scenario-based questions to give you a comprehensive understanding of Python programming. The questions are designed to mimic the ITS-303 certification exam, so you can get a feel for the types of questions you may encounter on the actual test. The explanations provided for each question are thorough and detailed, helping you to understand the underlying concepts and principles of Python programming. Additionally, the explanations include references to relevant Python documentation, so you can further your understanding of the language. Learning Python is a great investment for anyone who wants to pursue a career in software development or data analysis, or who wants to expand their skill set in these fields.

Название: Machine Learning Crash Course for Engineers
Автор: Eklas Hossain
Издательство: Springer
Год: 2024
Страниц: 465
Язык: английский
Формат: pdf
Размер: 12.7 MB

​Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to Machine Learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of Machine Learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement Machine Learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of Machine Learning quickly. Python and R are the two most highly used programming languages for developing ML programs. Python will be used as the only programming language in all examples of this book due to its ease of use, popularity, and the large, friendly, helpful, and interactive community Python encompasses. It is open-source, highly used in academic and research-based works, and is recommended by experts in almost every field. It is very efficient in terms of the amount of code needed to be written. The short, simple lines of Python code with obvious implications can be easily handled by beginners and are easy to read, debug, and expand. Python is also a cross-platform programming language, implying that it can run well on all operating systems and computers. The most used ML libraries are NumPy, Pandas, Scipy, Theano, Keras, Scikit-learn, Matplotlib, etc., while the most common frameworks are PyTorch and TensorFlow.

Название: Understanding Deep Learning
Автор: Simon J.D. Prince
Издательство: The MIT Press
Год: December 5, 2023
Страниц: 544
Язык: английский
Формат: pdf (true), epub (true)
Размер: 21.3 MB, 37.9 MB

An authoritative, accessible, and up-to-date treatment of Deep Learning that strikes a pragmatic middle ground between theory and practice. Deep Learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.