Mechanical Drawing
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A Complete History of the Druids
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Mechanical Drawing Self-Taught
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A Northern Summer; Or, Travels Round the Baltic, Through Denmark, Sweden, Russia, Prussia, and Part of Germany
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A History of the Ancient World
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Mechanical Drawing
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it.This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
A Narrative of the Campaign of the British Army in Spain
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A Northern Summer; Or, Travels Round the Baltic, Through Denmark, Sweden, Russia, Prussia, and Part of Germany
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Elements of General Drafting for Mechanical Engineers
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A History of Texas and Texans
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A History of Herkimer County
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A Topographical and Historical Account of Wainfleet and the Wapentake of Candleshoe, in the County of Lincoln
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Hidden and Devalued Feminized Labour in the Digital Humanities
Hidden and Devalued Feminized Labour in the Digital Humanities examines the data-driven labour that underpinned the Index Thomisticus-a preeminent project of the incunabular digital humanities-and advanced the data-foundations of computing in the Humanities.
Computational Thinking: A Perspective on Computer Science
This textbook is intended as a textbook for one-semester, introductory computer science courses aimed at undergraduate students from all disciplines. Self-contained and with no prerequisites, it focuses on elementary knowledge and thinking models. The content has been tested in university classrooms for over six years, and has been used in summer schools to train university and high-school teachers on teaching introductory computer science courses using computational thinking. This book introduces computer science from a computational thinking perspective. In computer science the way of thinking is characterized by three external and eight internal features, including automatic execution, bit-accuracy and abstraction. The book is divided into chapters on logic thinking, algorithmic thinking, systems thinking, and network thinking. It also covers societal impact and responsible computing material - from ICT industry to digital economy, from the wonder of exponentiation to wonder of cyberspace, and from code of conduct to best practices for independent work. The book's structure encourages active, hands-on learning using the pedagogic tool Bloom's taxonomy to create computational solutions to over 200 problems of varying difficulty. Students solve problems using a combination of thought experiment, programming, and written methods. Only 300 lines of code in total are required to solve most programming problems in this book.
A History of Herkimer County
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A History of the Cemetery of Mount Auburn
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Intelligent Computing for Big Data
Recent advances in artificial intelligence have the potential to further develop current big data research. The Special Issue on 'Intelligent Computing for Big Data' highlighted a number of recent studies related to the use of intelligent computing techniques in the processing of big data for text mining, autism diagnosis, behaviour recognition, and blockchain-based storage.
Practical Aspects of Declarative Languages
This book constitutes the proceedings of the 25th International Symposium on Practical Aspects of Declarative Languages, PADL 2023, which was held in Boston, MA, USA, in January 2023.The 15 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 36 submissions. The papers are organized in the following topical sections: Functional Programming; Logic Programming.
Nonlinear Dimensionality Reduction Techniques
This book proposes tools for analysis of multidimensional and metric data, by establishing a state-of-the-art of the existing solutions and developing new ones. It mainly focuses on visual exploration of these data by a human analyst, relying on a 2D or 3D scatter plot display obtained through Dimensionality Reduction. Performing diagnosis of an energy system requires identifying relations between observed monitoring variables and the associated internal state of the system. Dimensionality reduction, which allows to represent visually a multidimensional dataset, constitutes a promising tool to help domain experts to analyse these relations. This book reviews existing techniques for visual data exploration and dimensionality reduction such as tSNE and Isomap, and proposes new solutions to challenges in that field. In particular, it presents the new unsupervised technique ASKI and the supervised methods ClassNeRV and ClassJSE. Moreover, MING, a new approach for local map quality evaluation is also introduced. These methods are then applied to the representation of expert-designed fault indicators for smart-buildings, I-V curves for photovoltaic systems and acoustic signals for Li-ion batteries.
Mathematics and Its Applications in Science and Engineering
Natural language processing (NLP) is one of the most important technologies in use today, especially due to the large and growing amount of online text, which needs to be understood in order to fully ascertain its enormous value. During the last decade, the application of machine learning techniques has led to achievement of higher accuracy in many types of NLP applications. Although numerous machine learning models have been developed for NLP applications, deep learning approaches have recently achieved remarkable results across many NLP tasks. The present book contains all the articles accepted and published in the Special Issue "Natural Language Processing (NLP) and Machine Learning (ML)-Theory and Applications" of the MDPI journal Mathematics. A large range of research topics of NLP have been touched upon within this Special Issue, showcasing the diversity and dynamics of this perpetually evolving field, which is providing one of the most important technologies in use today. This Special Issue has provided a platform for researchers to present their novel work in the domain of NLP and its applications, with a focus on applications of machine learning and deep learning in this field. We hope that this will help to foster future research in NLP and all its related
Recent Advances in Nanomagnetism
The Special Issue on Recent Advances in Nanomagnetism is a compilation of articles, addressing various aspects of magnetic properties and behaviour in low dimensional magnetic materials. One contribution addresses the novel magnetic properties in a nanohybrid of iron oxide and carbide nanoparticles grown in diamond. Magnetic textures, such as skyrmion structures, form an important area of research in nanomagnetism, this forms the topic of another contribution. Several aspects of magnetisation dynamics are addressed in other contributions and relate to the developments of microresonators and microantennas applied to the study of magnetic nanostructures; the ferromagnetic resonance behaviour in nanodot systems are also considered. Materials development forms an important area of study in nanomagnetism, and, as such, the preparation conditions, such as annealing under an applied field, can have important effects on the magnetic properties of thin films and low dimensional structures. Such considerations form the study of one of the contributions. Perpendicular magnetic anisotropy has a number of important applications in magnetic storage materials; this is the subject of two further contributions.
A Christmas Carol in Prose
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A Treatise On Ecclesiastical Heraldry
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A Youthful Man-O'-Warsman
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A History Of Herkimer County
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A History of the old Town of Stratford and the City of Bridgeport, Connecticut
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Advances in Information Security and Privacy
With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue.
Natural Language Processing (NLP) and Machine Learning (ML)
Natural language processing (NLP) is one of the most important technologies in use today, especially due to the large and growing amount of online text, which needs to be understood in order to fully ascertain its enormous value. During the last decade, the application of machine learning techniques has led to achievement of higher accuracy in many types of NLP applications. Although numerous machine learning models have been developed for NLP applications, deep learning approaches have recently achieved remarkable results across many NLP tasks. The present book contains all the articles accepted and published in the Special Issue "Natural Language Processing (NLP) and Machine Learning (ML)-Theory and Applications" of the MDPI journal Mathematics. A large range of research topics of NLP have been touched upon within this Special Issue, showcasing the diversity and dynamics of this perpetually evolving field, which is providing one of the most important technologies in use today. This Special Issue has provided a platform for researchers to present their novel work in the domain of NLP and its applications, with a focus on applications of machine learning and deep learning in this field. We hope that this will help to foster future research in NLP and all its related
Applying Math with Python - Second Edition
Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey Features: Compute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to use Python libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook Description: The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What You Will Learn: Become familiar with basic Python packages, tools, and libraries for solving mathematical problemsExplore real-world applications of mathematics to reduce a problem in optimizationUnderstand the core concepts of applied mathematics and their application in computer scienceFind out how to choose the most suitable package, tool, or technique to solve a problemImplement basic mathematical plotting, change plot styles, and add labels to plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is for: Whether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.
A Clinical Manual Of Mental Diseases
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A Hundred Battles in the West
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The Imperial Administrative System in the Ninth Century
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A History of England, by Mrs Markham 12Th Ed. Authors Ed., Revised
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Mathematical Drawing and Measuring Instruments
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Automata Old and New
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Introduction to Image Analysis using Python
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. There are two types of methods used for image processing namely, analogue and digital image processing. Analogue image processing can be used for the hard copies like printouts and photographs. Image analysts use various fundamentals of interpretation while using these visual techniques. Digital image processing techniques help in manipulation of the digital images by using computers. The three general phases that all types of data have to undergo while using digital technique are preprocessing, enhancement, and display, information extraction using python.
Automata Old and New
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A Commentary On the New Code of Canon Law; Volume 2
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Defence Application of Artificial Intelligence & Machine Learning
The ultimate goal of the current digital society extreme automation is to enable artificial intelligence (AI) autonomous Unmanned Aerial Vehicles (UAVs) modelled with Machine Learning to respond expeditiously in the security surveillance in several missions beyond visually line of sight (BVLOS) which are the fundamental aspect of modern warfare controlled through the cloud infrastructure in Internet of Thing (IoTs) computing. When contrasted with other conventional weapon systems, the military defence systems furnished with Artificial Intelligence and Machine Learning are proficient in managing extensive volumes of data set more powerfully. Moreover, Artificial Intelligence enhances self-learning for all autonomous UAVs self-awareness, self-control, self-actuation, self-protection and self-regulation for defensive missions, owing to its intrinsic computing and decision-making competencies. The defence application of Artificial Intelligence is almost in every military intelligence formation with improved military research and development funding to advance new and progressive applications for AI driven systems in military combatant logistics for process modernism.
