Advertisement
| free quantum machine learning solutions: Hands-On Quantum Machine Learning With Python Frank Zickert, 2021-06-19 You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think. Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you'll need to implement real-world solutions. Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner. |
| free quantum machine learning solutions: Quantum Machine Learning: An Applied Approach Santanu Ganguly, 2021-08-11 Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author’s active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. What You will Learn Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive Who This Book Is For Data scientists, machine learning professionals, and researchers |
| free quantum machine learning solutions: Quantum Machine Learning Peter Wittek, 2014-09-10 Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. - Bridges the gap between abstract developments in quantum computing with the applied research on machine learning - Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing - Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research |
| free quantum machine learning solutions: Machine Learning with Quantum Computers Maria Schuld, Francesco Petruccione, 2021-10-17 This book offers an introduction into quantum machine learning research, covering approaches that range from near-term to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years. |
| free quantum machine learning solutions: Supervised Learning with Quantum Computers Maria Schuld, Francesco Petruccione, 2019-10-06 Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices. |
| free quantum machine learning solutions: Quantum Machine Learning Pethuru Raj, Houbing Herbert Song, Dac-Nhuong Le, Narayan Vyas, 2024-08-05 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. |
| free quantum machine learning solutions: Quantum Machine Learning Peter Wittek, 2016-08-19 Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. |
| free quantum machine learning solutions: Quantum Machine Learning Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti, 2020-06-08 Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. |
| free quantum machine learning solutions: Quantum Computing Solutions Bhagvan Kommadi, 2020 Know how to use quantum computing solutions involving artificial intelligence (AI) algorithms and applications across different disciplines. Quantum solutions involve building quantum algorithms that improve computational tasks within quantum computing, AI, data science, and machine learning. As opposed to quantum computer innovation, quantum solutions offer automation, cost reduction, and other efficiencies to the problems they tackle. Starting with the basics, this book covers subsystems and properties as well as the information processing network before covering quantum simulators. Solutions such as the Traveling Salesman Problem, quantum cryptography, scheduling, and cybersecurity are discussed in step-by-step detail. The book presents code samples based on real-life problems in a variety of industries, such as risk assessment and fraud detection in banking. In pharma, you will look at drug discovery and protein-folding solutions. Supply chain optimization and purchasing solutions are presented in the manufacturing domain. In the area of utilities, energy distribution and optimization problems and solutions are explained. Advertising scheduling and revenue optimization solutions are included from media and technology verticals. You will: Understand the mathematics behind quantum computing Know the solution benefits, such as automation, cost reduction, and efficiencies Be familiar with the quantum subsystems and properties, including states, protocols, operations, and transformations Be aware of the quantum classification algorithms: classifiers, and support and sparse support vector machines Use AI algorithms, including probability, walks, search, deep learning, and parallelism. |
| free quantum machine learning solutions: Quantum Computation and Quantum Information Michael A. Nielsen, Isaac L. Chuang, 2010-12-09 One of the most cited books in physics of all time, Quantum Computation and Quantum Information remains the best textbook in this exciting field of science. This 10th anniversary edition includes an introduction from the authors setting the work in context. This comprehensive textbook describes such remarkable effects as fast quantum algorithms, quantum teleportation, quantum cryptography and quantum error-correction. Quantum mechanics and computer science are introduced before moving on to describe what a quantum computer is, how it can be used to solve problems faster than 'classical' computers and its real-world implementation. It concludes with an in-depth treatment of quantum information. Containing a wealth of figures and exercises, this well-known textbook is ideal for courses on the subject, and will interest beginning graduate students and researchers in physics, computer science, mathematics, and electrical engineering. |
| free quantum machine learning solutions: Learn Quantum Computing with Python and Q# Sarah C. Kaiser, Christopher Grenade, 2021-07-27 Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Summary Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you’ll build your own quantum simulator and apply quantum programming techniques to real-world examples including cryptography and chemical analysis. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Quantum computers present a radical leap in speed and computing power. Improved scientific simulations and new frontiers in cryptography that are impossible with classical computing may soon be in reach. Microsoft’s Quantum Development Kit and the Q# language give you the tools to experiment with quantum computing without knowing advanced math or theoretical physics. About the book Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Use Python to build your own quantum simulator and take advantage of Microsoft’s open source tools to fine-tune quantum algorithms. The authors explain complex math and theory through stories, visuals, and games. You’ll learn to apply quantum to real-world applications, such as sending secret messages and solving chemistry problems. What's inside The underlying mechanics of quantum computers Simulating qubits in Python Exploring quantum algorithms with Q# Applying quantum computing to chemistry, arithmetic, and data About the reader For software developers. No prior experience with quantum computing required. About the author Dr. Sarah Kaiser works at the Unitary Fund, a non-profit organization supporting the quantum open-source ecosystem, and is an expert in building quantum tech in the lab. Dr. Christopher Granade works in the Quantum Systems group at Microsoft, and is an expert in characterizing quantum devices. Table of Contents PART 1 GETTING STARTED WITH QUANTUM 1 Introducing quantum computing 2 Qubits: The building blocks 3 Sharing secrets with quantum key distribution 4 Nonlocal games: Working with multiple qubits 5 Nonlocal games: Implementing a multi-qubit simulator 6 Teleportation and entanglement: Moving quantum data around PART 2 PROGRAMMING QUANTUM ALGORITHMS IN Q# 7 Changing the odds: An introduction to Q# 8 What is a quantum algorithm? 9 Quantum sensing: It’s not just a phase PART 3 APPLIED QUANTUM COMPUTING 10 Solving chemistry problems with quantum computers 11 Searching with quantum computers 12 Arithmetic with quantum computers |
| free quantum machine learning solutions: Machine Learning Meets Quantum Physics Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller, 2020-06-03 Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context. |
| free quantum machine learning solutions: Quantum Processes Systems, and Information Benjamin Schumacher, Michael Westmoreland, 2010-03-25 A new and exciting approach to the basics of quantum theory, this undergraduate textbook contains extensive discussions of conceptual puzzles and over 800 exercises and problems. Beginning with three elementary 'qubit' systems, the book develops the formalism of quantum theory, addresses questions of measurement and distinguishability, and explores the dynamics of quantum systems. In addition to the standard topics covered in other textbooks, it also covers communication and measurement, quantum entanglement, entropy and thermodynamics, and quantum information processing. This textbook gives a broad view of quantum theory by emphasizing dynamical evolution, and exploring conceptual and foundational issues. It focuses on contemporary topics, including measurement, time evolution, open systems, quantum entanglement, and the role of information. |
| free quantum machine learning solutions: Classical and Quantum Information Dan C. Marinescu, 2011-01-07 A new discipline, Quantum Information Science, has emerged in the last two decades of the twentieth century at the intersection of Physics, Mathematics, and Computer Science. Quantum Information Processing is an application of Quantum Information Science which covers the transformation, storage, and transmission of quantum information; it represents a revolutionary approach to information processing. Classical and Quantum Information covers topics in quantum computing, quantum information theory, and quantum error correction, three important areas of quantum information processing. Quantum information theory and quantum error correction build on the scope, concepts, methodology, and techniques developed in the context of their close relatives, classical information theory and classical error correcting codes. - Presents recent results in quantum computing, quantum information theory, and quantum error correcting codes - Covers both classical and quantum information theory and error correcting codes - The last chapter of the book covers physical implementation of quantum information processing devices - Covers the mathematical formalism and the concepts in Quantum Mechanics critical for understanding the properties and the transformations of quantum information |
| free quantum machine learning solutions: Machine Learning for Cyber Agents Stanislav Abaimov, Maurizio Martellini, 2022-01-27 The cyber world has been both enhanced and endangered by AI. On the one hand, the performance of many existing security services has been improved, and new tools created. On the other, it entails new cyber threats both through evolved attacking capacities and through its own imperfections and vulnerabilities. Moreover, quantum computers are further pushing the boundaries of what is possible, by making machine learning cyber agents faster and smarter. With the abundance of often-confusing information and lack of trust in the diverse applications of AI-based technologies, it is essential to have a book that can explain, from a cyber security standpoint, why and at what stage the emerging, powerful technology of machine learning can and should be mistrusted, and how to benefit from it while avoiding potentially disastrous consequences. In addition, this book sheds light on another highly sensitive area – the application of machine learning for offensive purposes, an aspect that is widely misunderstood, under-represented in the academic literature and requires immediate expert attention. |
| free quantum machine learning solutions: Financial Modeling Using Quantum Computing Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere, 2023-05-31 Achieve optimized solutions for real-world financial problems using quantum machine learning algorithms Key Features Learn to solve financial analysis problems by harnessing quantum power Unlock the benefits of quantum machine learning and its potential to solve problems Train QML to solve portfolio optimization and risk analytics problems Book DescriptionQuantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems. This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you’ll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing. By the end of this book, you’ll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.What you will learn Explore framework, model and technique deployed for Quantum Computing Understand the role of QC in financial modeling and simulations Apply Qiskit and Pennylane framework for financial modeling Build and train models using the most well-known NISQ algorithms Explore best practices for writing QML algorithms Use QML algorithms to understand and solve data mining problems Who this book is for This book is for financial practitioners, quantitative analysts, or developers; looking to bring the power of quantum computing to their organizations. This is an essential resource written for finance professionals, who want to harness the power of quantum computers for solving real-world financial problems. A basic understanding of Python, calculus, linear algebra, and quantum computing is a prerequisite. |
| free quantum machine learning solutions: Advanced Computing and Intelligent Technologies Monica Bianchini, Vincenzo Piuri, Sanjoy Das, Rabindra Nath Shaw, 2021-07-21 This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2021) held at NCR New Delhi, India, during March 20–21, 2021, jointly organized by Galgotias University, India, and Department of Information Engineering and Mathematics Università Di Siena, Italy. It discusses emerging topics pertaining to advanced computing, intelligent technologies, and networks including AI and machine learning, data mining, big data analytics, high-performance computing network performance analysis, Internet of things networks, wireless sensor networks, and others. The book offers a valuable asset for researchers from both academia and industries involved in advanced studies. |
| free quantum machine learning solutions: Quantum Computing in Practice with Qiskit® and IBM Quantum Experience® Hassi Norlén, 2020-11-20 Understand the nuances of programming traditional quantum computers and solve the challenges of the future while building and executing quantum programs on IBM Quantum hardware and simulators Key Features Work your way up from writing a simple quantum program to programming complex quantum algorithms Explore the probabilistic nature of qubits by performing quantum coin tosses and using random number generators Delve into quantum algorithms and their practical applications in various domains Long Description IBM Quantum Experience(R) is a leading platform for programming quantum computers and implementing quantum solutions directly on the cloud. This book will help you get up to speed with programming quantum computers and provide solutions to the most common problems and challenges. You'll start with a high-level overview of IBM Quantum Experience(R) and Qiskit(R), where you will perform the installation while writing some basic quantum programs. This introduction puts less emphasis on the theoretical framework and more emphasis on recent developments such as Shor's algorithm and Grover's algorithm. Next, you'll delve into Qiskit(R), a quantum information science toolkit, and its constituent packages such as Terra, Aer, Ignis, and Aqua. You'll cover these packages in detail, exploring their benefits and use cases. Later, you'll discover various quantum gates that Qiskit(R) offers and even deconstruct a quantum program with their help, before going on to compare Noisy Intermediate-Scale Quantum (NISQ) and Universal Fault-Tolerant quantum computing using simulators and actual hardware. Finally, you'll explore quantum algorithms and understand how they differ from classical algorithms, along with learning how to use pre-packaged algorithms in Qiskit(R) Aqua. By the end of this quantum computing book, you'll be able to build and execute your own quantum programs using IBM Quantum Experience(R) and Qiskit(R) with Python. What You Will Learn Visualize a qubit in Python and understand the concept of superposition Install a local Qiskit(R) simulator and connect to actual quantum hardware Compose quantum programs at the level of circuits using Qiskit(R) Terra Compare and contrast Noisy Intermediate-Scale Quantum computing (NISQ) and Universal Fault-Tolerant quantum computing using simulators and IBM Quantum(R) hardware Mitigate noise in quantum circuits and systems using Qiskit(R) Ignis Understand the difference between classical and quantum algorithms by implementing Grover's algorithm in Qiskit(R) Who this book is for This book is for developers, data scientists, machine learning researchers, or quantum computing enthusiasts who want to understand how to use IBM Quantum Experience(R) and Qiskit(R) to implement quantum solutions and gain practical quantum computing experience. Python programming experience is a must to grasp the concepts covered in the book more effectively. Basic knowledge of quantum computing will also be beneficial. |
| free quantum machine learning solutions: Artificial Intelligence and Speech Technology Arun Sharma, Ritu Rani, 2025-05-29 This two-volume set, CCIS 2389 and CCIS 2390, constitutes selected papers presented at the 6th International Conference on Artificial Intelligence and Speech Technology, AIST 2024, held in Delhi, India, during November 13–14, 2024. The 40 full papers and 15 short papers presented in these proceedings were carefully reviewed and selected from 398 submissions.These papers focus on Speech Technology using AI and AI innovations for CV and NLP. They have been categorized under the following topical sections:- Part I : Trends and Applications in Speech Processing; Recent Trends in Speech and NLP; Emerging trends in Speech Processing; Advances in Computational Linguistics and NLP. Part II : Recent Trends in Machine Learning and Deep Learning; Analysis using Hybrid technologies with Artificial Intelligence; Exploring New Horizons in Computer Vision Research. |
| free quantum machine learning solutions: Dancing with Qubits Robert S. Sutor, 2019-11-28 Explore the principles and practicalities of quantum computing Key Features Discover how quantum computing works and delve into the math behind it with this quantum computing textbook Learn how it may become the most important new computer technology of the century Explore the inner workings of quantum computing technology to quickly process complex cloud data and solve problems Book DescriptionQuantum computing is making us change the way we think about computers. Quantum bits, a.k.a. qubits, can make it possible to solve problems that would otherwise be intractable with current computing technology. Dancing with Qubits is a quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. From there it moves on to a fuller description of classical computing and the mathematical underpinnings necessary to understand such concepts as superposition, entanglement, and interference. Next up is circuits and algorithms, both basic and more sophisticated. It then nicely moves on to provide a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments will affect you. Really understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is introduced and explained thoroughly, in clear English with helpful examples.What you will learn See how quantum computing works, delve into the math behind it, what makes it different, and why it is so powerful with this quantum computing textbook Discover the complex, mind-bending mechanics that underpin quantum systems Understand the necessary concepts behind classical and quantum computing Refresh and extend your grasp of essential mathematics, computing, and quantum theory Explore the main applications of quantum computing to the fields of scientific computing, AI, and elsewhere Examine a detailed overview of qubits, quantum circuits, and quantum algorithm Who this book is for Dancing with Qubits is a quantum computing textbook for those who want to deeply explore the inner workings of quantum computing. This entails some sophisticated mathematical exposition and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, and computer science. |
| free quantum machine learning solutions: New Opportunities for Innovation Breakthroughs for Developing Countries and Emerging Economies Rachid Benmoussa, Roland De Guio, Sébastien Dubois, Sebastian Koziołek, 2019-10-03 This book constitutes the refereed proceedings of the 19th International TRIZ Future Conference on Automated Invention for Smart Industries, held in Marrakesh, Morocco, in October 2019 and sponsored by IFIP WG 5.4. The 41 full papers presented were carefully reviewed and selected from 72 submissions. They are organized in seven thematic sections: TRIZ improvement: theory, methods and tools; TRIZ and other innovation approaches; TRIZ applications in technical design; TRIZ applications in eco design; TRIZ applications in software engineering; TRIZ applications in specific disciplinary fields; and TRIZ in teaching. |
| free quantum machine learning solutions: Advances in Artificial-Business Analytics and Quantum Machine Learning K. C. Santosh, Sandeep Kumar Sood, Hari Mohan Pandey, Charu Virmani, 2024-09-18 This book presents select proceedings of the 3rd International Conference on “Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects” (Com-IT-Con 2023) held at the Manav Rachna University in July 2023. It covers topics such as artificial intelligence and business analytics, virtual/augmented reality, quantum information systems, cyber security, data science, and machine learning. The book is useful for researchers and professionals interested in the broad field of communication engineering. |
| free quantum machine learning solutions: Shaping Cutting-Edge Technologies and Applications for Digital Banking and Financial Services Alex Khang, 2025-01-31 Cutting-edge technologies have recently shown great promise in a variety of activities for enhancing the existing services of a bank such as the improvement of transactions, ensuring that transactions are done correctly, and managing records of services of savings accounts, loan and mortgage services, wealth management, providing credit and debit cards, overdraft services and physical evidence as key drivers of bank ecosystem. In the financial world, emerging analytics and prediction tools can be used to analyze and visualize structured data, such as financial market data, and to forecast future trends that can be supported by leaders to make informed decisions about investment strategies. This book explores the importance of artificial intelligence (AI)-based predictive analytics tools in the financial services industry and their role in combating financial fraud. As fintech continues to revolutionize the financial landscape, it also brings forth new challenges, including sophisticated fraudulent activities. Therefore, this book shares the problem of enhancing fraud detection and prevention through the application of predictive analytics. This book contributes to a deeper understanding of the importance of predictive analytics in the finance field and its pivotal role in cybersecurity and combating fraud. It provides valuable insights for the financial services industry, researchers, and policymakers, aiming to fortify the security and resilience of financial systems in the face of evolving financial fraud challenges. Cuurently, AI has replaced recurrent intellectual decisions due to the availability of information and its access. These changes have created a revolution in financial operations resulting in environmental variations in the banking and finance sectors. Likewise, analytics transformed the not only finance field but also banking as it is increasing the transparency of lending-related activities. In addition, this book provides a set of tools for complex analyses of people-related data and through a variety of statistical analysis techniques ranging from simple descriptive statistics to machine learning, HR analytics enables performance evaluation and increases the transparency of finance transactions as well as the problems, advantages, and disadvantages of new digital transformation. The book is not merely a compilation of technical knowledge; it is a beacon of innovation that beckons readers to envision a future where cutting-edge technologies and finance services intertwine seamlessly. With its engaging and thought-provoking content, the book leaves an indelible impression, urging readers to embrace the transformative power of technology and embark on a collective mission to unlock the full potential of fintech for the betterment of humanity. |
| free quantum machine learning solutions: Pattern Recognition and Machine Learning Christopher M. Bishop, 2006-08-17 This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. |
| free quantum machine learning solutions: Quantum Computing Richard Skiba, 2025-03-09 Quantum computing is revolutionizing the way we approach computation, unlocking unprecedented capabilities that challenge the limits of classical computers. This book is a guide for anyone seeking to understand the complexities of this emerging field. Whether you are a student, researcher, or technology enthusiast, this book provides a comprehensive exploration of the core concepts, algorithms, hardware, and real-world applications of quantum computing. The book begins with a solid introduction to quantum mechanics, the foundational science that makes quantum computing possible. Readers will gain an understanding of key principles such as superposition, entanglement, and quantum interference—phenomena that enable quantum computers to process vast amounts of data in parallel. The discussion then shifts to qubits, the fundamental building blocks of quantum computation, explaining their physical implementations using superconducting circuits, trapped ions, photonic systems, and other technologies. As the book progresses, it delves into the architecture of quantum computing, covering quantum logic gates, circuit design, and universal gate sets that define how quantum computers perform calculations. It explores groundbreaking quantum algorithms such as Shor’s algorithm for factoring large numbers and Grover’s algorithm for quantum search, demonstrating how quantum computing achieves exponential speedup over classical methods. A key challenge in quantum computing is error correction and noise mitigation. This book provides an in-depth analysis of quantum error correction techniques, fault-tolerant computing, and strategies for overcoming decoherence and environmental noise, making it accessible even to readers without a deep background in physics. The discussion extends beyond theory to practical implementations, examining quantum hardware developed by leading technology companies such as IBM, Google, Microsoft, IonQ, and Rigetti. It also explores the role of quantum computing in cybersecurity, highlighting quantum cryptography, post-quantum encryption, and quantum key distribution protocols that ensure secure communications. The latter sections of the book showcase real-world applications of quantum computing in fields such as artificial intelligence, drug discovery, climate modelling, financial modelling, and supply chain optimization. It also covers cloud-based quantum computing services like IBM Quantum Experience, Google Quantum AI, and Amazon Braket, providing insights into how organizations can leverage quantum computing without owning expensive quantum hardware. With an eye toward the future, Quantum Computing: Fundamental Principles of Quantum Computing Systems examines the ongoing challenges and expected advancements in the field, offering predictions on the evolution of quantum computing over the next two decades. This book is an indispensable resource for understanding one of the most exciting technological frontiers of our time. |
| free quantum machine learning solutions: Graph Representation Learning William L. Hamilton, 2022-06-01 Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning. |
| free quantum machine learning solutions: Tiny Quantum, Giant Revolution Ching-ray Chang, Meng-chien Wang, 2024-07-02 The first industrial revolution was accompanied by the emergence of the School of Engineering, the second brought about the School of Electrical Engineering, and the third digital revolution led to the School of Information. It is now obvious that the change in this quantum revolution must lead to the emergence of the School of Quantum Science and Technology. Quantum technology can develop comprehensively through the integration of science, technology, engineering, art and mathematics (STEAM). In 1925, Heisenberg developed the Matrix Mechanics, cracking the mysteries of the Quantum world. Coinciding with the 100th anniversary of Matrix Mechanics, in 2024, the General Assembly of the United Nations proclaimed 2025 the 'International Year of Quantum Science and Technology,' along with a year-long initiative celebrating the profound impacts of quantum science on technology, culture, and our understanding of the natural world.This book outlines the importance of the 'Second Quantum Revolution,' introduces quantum computers, quantum communications, and quantum sensors, and then provides a framework for the emergence of the quantum Internet of Things. What basic quantum literacy should modern citizens have in this era? The 'Second Quantum Revolution,' where quantum knowledge and engineering technology are once again combined, will provide faster, more effective, and more sensitive quantum facilities to accelerate cross-field exploration, and will also make human life more comfortable and convenient than ever before.In the 'First Quantum Revolution' in the 20th century, humans learned quantum science from nature and used existing materials to make quantum components. In the 'Second Quantum Revolution' in the 21st century, humans further used quantum science to construct quantum engineering. We now make materials and components that are not found in nature and assemble new quantum machines to benefit mankind! This is the stage of a glorious quantum era, which is a hundred times more brilliant than the past classical physics era. |
| free quantum machine learning solutions: Quantum Computing: An Applied Approach Jack D. Hidary, 2021-11-03 This book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these elements together in an updated manner. This work is suitable for both academic coursework and corporate technical training. The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more. This new edition benefits from the input of the many faculty, students, corporate engineering teams, and independent readers who have used the first edition. This volume comprises three books under one cover: Part I outlines the necessary foundations of quantum computing and quantum circuits. Part II walks through the canon of quantum computing algorithms and provides code on a range of quantum computing methods in current use. Part III covers the mathematical toolkit required to master quantum computing. Additional resources include a table of operators and circuit elements and a companion GitHub site providing code and updates. Jack D. Hidary is a research scientist in quantum computing and in AI at Alphabet X, formerly Google X. |
| free quantum machine learning solutions: A First Course in Machine Learning Simon Rogers, Mark Girolami, 2016-10-14 Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ |
| free quantum machine learning solutions: Handbook of Machine Learning for Computational Optimization Vishal Jain, Sapna Juneja, Abhinav Juneja, Ramani Kannan, 2021-11-02 Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers. |
| free quantum machine learning solutions: Applied Computer-Aided Drug Design: Models and Methods Igor José dos Santos Nascimento, 2023-12-08 Designing and developing new drugs is an expensive and time-consuming process, and there is a need to discover new tools or approaches that can optimize this process. Applied Computer-Aided Drug Design: Models and Methods compiles information about the main advances in computational tools for discovering new drugs in a simple and accessible language for academic students to early career researchers. The book aims to help readers understand how to discover molecules with therapeutic potential by bringing essential information about the subject into one volume. Key Features · Presents the concepts and evolution of classical techniques, up to the use of modern methods based on computational chemistry in accessible format. · Gives a primer on structure- and ligand-based drug design and their predictive capacity to discover new drugs. · Explains theoretical fundamentals and applications of computer-aided drug design. · Focuses on a range of applications of the computations tools, such as molecular docking; molecular dynamics simulations; homology modeling, pharmacophore modeling, quantitative structure-activity relationships (QSAR), density functional theory (DFT), fragment-based drug design (FBDD), and free energy perturbation (FEP). · Includes scientific reference for advanced readers Readership Students, teachers and early career researchers. |
| free quantum machine learning solutions: Dew Computing Debashis De, Samarjit Roy, 2023-09-02 This book discusses the dew computing paradigm with the evolution of future-generation technologies through the cloud and the Internet of Things in the scope of machine intelligence. Dew computing is an emerging paradigm that inherits a flexible and super-hybrid methodology to afford personal information to users with self-regulating internetwork connectivity. The contents conceptualize how the end-users can benefit from data analytics through intelligent data sensing, computing, analytics, and distributed scenarios using a dew-cloud computational framework over the Internet of Things environment. The main focus of this book is to bring all the related technologies into a single platform so that undergraduate and postgraduate students, researchers, academicians, and the industry can easily understand dew computing, future generations of cloud computing, machine intelligence, and representation learning in IoT-enabled technologies. |
| free quantum machine learning solutions: Information Theory, Inference and Learning Algorithms David J. C. MacKay, 2003-09-25 Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning. |
| free quantum machine learning solutions: Principles of Quantum Artificial Intelligence Andreas Wichert, 2020-07 |
| free quantum machine learning solutions: Machine Learning - A Journey To Deep Learning: With Exercises And Answers Andreas Miroslaus Wichert, Luis Sa-couto, 2021-01-26 This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.Related Link(s) |
| free quantum machine learning solutions: Hands-On Machine Learning with Azure Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, 2018-10-31 Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies Key FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook Description Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models. What you will learnDiscover the benefits of leveraging the cloud for ML and AIUse Cognitive Services APIs to build intelligent botsBuild a model using canned algorithms from Microsoft and deploy it as a web serviceDeploy virtual machines in AI development scenariosApply R, Python, SQL Server, and Spark in AzureBuild and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlowImplement model retraining in IoT, Streaming, and Blockchain solutionsExplore best practices for integrating ML and AI functions with ADLA and logic appsWho this book is for If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book |
| free quantum machine learning solutions: Quantum Computing National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Intelligence Community Studies Board, Computer Science and Telecommunications Board, Committee on Technical Assessment of the Feasibility and Implications of Quantum Computing, 2019-03-27 Quantum mechanics, the subfield of physics that describes the behavior of very small (quantum) particles, provides the basis for a new paradigm of computing. First proposed in the 1980s as a way to improve computational modeling of quantum systems, the field of quantum computing has recently garnered significant attention due to progress in building small-scale devices. However, significant technical advances will be required before a large-scale, practical quantum computer can be achieved. Quantum Computing: Progress and Prospects provides an introduction to the field, including the unique characteristics and constraints of the technology, and assesses the feasibility and implications of creating a functional quantum computer capable of addressing real-world problems. This report considers hardware and software requirements, quantum algorithms, drivers of advances in quantum computing and quantum devices, benchmarks associated with relevant use cases, the time and resources required, and how to assess the probability of success. |
| free quantum machine learning solutions: Data Management, Analytics and Innovation Neha Sharma, Amlan Chakrabarti, Valentina Emilia Balas, Jan Martinovic, 2020-09-18 This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. |
| free quantum machine learning solutions: Pandas in 7 Days Fabio Nelli, 2022-04-25 Make data analysis fast, reliable, and clean with Python, Pandas and Matplotlib. KEY FEATURES ● A detailed walk-through of the Pandas library's features with multiple examples. ● Numerous graphical representations and reporting capabilities using popular Matplotlib. ● A high-level overview of extracting data from including files, databases, and the web. DESCRIPTION No matter how large or small your dataset is, the author 'Fabio Nelli' simply used this book to teach all the finest technical coaching on applying Pandas to conduct data analysis with zero worries. Both newcomers and seasoned professionals will benefit from this book. It teaches you how to use the pandas library in just one week. Every day of the week, you'll learn and practise the features and data analysis exercises listed below: Day 01: Get familiar with the fundamental data structures of pandas, including Declaration, data upload, indexing, and so on. Day 02: Execute commands and operations related to data selection and extraction, including slicing, sorting, masking, iteration, and query execution. Day 03: Advanced commands and operations such as grouping, multi-indexing, reshaping, cross-tabulations, and aggregations. Day 04: Working with several data frames, including comparison, joins, concatenation, and merges. Day 05: Cleaning, pre-processing, and numerous strategies for data extraction from external files, the web, databases, and other data sources. Day 06: Working with missing data, interpolation, duplicate labels, boolean data types, text data, and time-series datasets. Day 07: Introduction to Jupyter Notebooks, interactive data analysis, and analytical reporting with Matplotlib's stunning graphics. WHAT YOU WILL LEARN ●Extract, cleanse, and process data from databases, text files, HTML pages, and JSON data. ●Work with DataFrames and Series, and apply functions to scale data manipulations. ●Graph your findings using charts typically used in modern business analytics. ●Learn to use all of the pandas basic and advanced features independently. ● Storing and manipulating labeled/columnar data efficiently. WHO THIS BOOK IS FOR If you're looking to expedite a data science or sophisticated data analysis project, you've come to the perfect place. Each data analysis topic is covered step-by-step with real-world examples. Python knowledge isn't required however, knowing a little bit helps. TABLE OF CONTENTS 1. Pandas, the Python library 2. Setting up a Data Analysis Environment 3. Day 1 - Data Structures in Pandas library 4. Day 2 - Working within a DataFrame, Basic Functionalities 5. Day 3 - Working within a DataFrame, Advanced Functionalities 6. Day 4 - Working with two or more DataFrames 7. Day 5 - Working with data sources and real-word datasets 8. Day 6 - Troubleshooting Challenges wit Real Datasets 9. Day 7 - Data Visualization and Reporting 10. Conclusion – Moving Beyond |
| free quantum machine learning solutions: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering M. Shamim Kaiser, Sajjad Waheed, Anirban Bandyopadhyay, Mufti Mahmud, Kanad Ray, 2023-05-27 This book presents various computational and cognitive modeling approaches in the areas of health, education, finance, environment, engineering, commerce, and industry. It is a collection of selected conference papers presented at the 4th International Conference on Trends in Cognitive Computation Engineering (TCCE 2022), hosted by Mawlana Bhashani Science and Technology University, Tangail, Bangladesh, during December 17–18, 2022. It shares cutting-edge insights and ideas from mathematicians, engineers, scientists, and researchers and discusses fresh perspectives on problem solving in a range of research areas. |
grammaticality - Is the phrase "for free" correct? - English …
Aug 16, 2011 · Because free by itself can function as an adverb in the sense "at no cost," some critics reject the phrase for free. A phrase such as for nothing, at no cost, or a similar …
"Free of" vs. "Free from" - English Language & Usage Stack Exchange
Apr 15, 2017 · So free from is used to indicate protection from something problematic, and free of (which doesn't correspond neatly to freedom of) is used to indicate the absence of something: …
orthography - Free stuff - "swag" or "schwag"? - English Language ...
My company gives out free promotional items with the company name on it. Is this stuff called company swag or schwag? It seems that both come up as common usages—Google …
How to ask about one's availability? "free/available/not busy"?
Saying free or available rather than busy may be considered a more "positive" enquiry. It may also simply mean that you expect the person to be busy rather than free, rather than the other way …
On Saturday afternoon or in the Saturday afternoon?
Sep 16, 2011 · The choice of prepositions depends upon the temporal context in which you're speaking. "On ~ afternoon" implies that the afternoon is a single point in time; thus, that …
"At/on (the) weekend (s)" - English Language & Usage Stack …
Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their …
meaning - What is free-form data entry? - English Language
If you are creating a column for free-form data entry, such as a notes column to hold data about customer interactions with your company’s customer service department, then varchar will …
word choice - What is the neutral way of telling someone to "do ...
Feb 13, 2014 · You're free to choose....and more. The choice between these depends a little bit on context. If you're trying to convey that you want them to choose, but that you don't need a …
word choice - English Language & Usage Stack Exchange
Aug 5, 2018 · Items given away free, typically for promotional purposes, to people attending an event, using a service, etc. It’s especially common in reference to, e.g., the very nice “swag …
etymology - Origin of the phrase "free, white, and twenty-one ...
May 20, 2022 · Bartlett Whiting, Modern Proverbs and Proverbial Sayings (1989) cites instances of "free, white and twenty-one" as a proverbial phrase going back to 1932, in Cecil Gregg, The …
grammaticality - Is the phrase "for free" correct? - English …
Aug 16, 2011 · Because free by itself can function as an adverb in the sense "at no cost," some critics reject the phrase for free. A phrase such as for nothing, at no cost, or a similar …
"Free of" vs. "Free from" - English Language & Usage Stack Exchange
Apr 15, 2017 · So free from is used to indicate protection from something problematic, and free of (which doesn't correspond neatly to freedom of) is used to indicate the absence of something: …
orthography - Free stuff - "swag" or "schwag"? - English Language ...
My company gives out free promotional items with the company name on it. Is this stuff called company swag or schwag? It seems that both come up as common usages—Google …
How to ask about one's availability? "free/available/not busy"?
Saying free or available rather than busy may be considered a more "positive" enquiry. It may also simply mean that you expect the person to be busy rather than free, rather than the other way …
On Saturday afternoon or in the Saturday afternoon?
Sep 16, 2011 · The choice of prepositions depends upon the temporal context in which you're speaking. "On ~ afternoon" implies that the afternoon is a single point in time; thus, that …
"At/on (the) weekend (s)" - English Language & Usage Stack …
Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their …
meaning - What is free-form data entry? - English Language
If you are creating a column for free-form data entry, such as a notes column to hold data about customer interactions with your company’s customer service department, then varchar will …
word choice - What is the neutral way of telling someone to "do ...
Feb 13, 2014 · You're free to choose....and more. The choice between these depends a little bit on context. If you're trying to convey that you want them to choose, but that you don't need a …
word choice - English Language & Usage Stack Exchange
Aug 5, 2018 · Items given away free, typically for promotional purposes, to people attending an event, using a service, etc. It’s especially common in reference to, e.g., the very nice “swag …
etymology - Origin of the phrase "free, white, and twenty-one ...
May 20, 2022 · Bartlett Whiting, Modern Proverbs and Proverbial Sayings (1989) cites instances of "free, white and twenty-one" as a proverbial phrase going back to 1932, in Cecil Gregg, The …
