Search results for "Andre Python"
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Data Structures and Program Design Using Python
Data structures provide a means to managing huge amounts of information such as large databases, using SEO effectively, and creating Internet/Web indexing services. This book is designed to present fundamentals of data structures for beginners using the Python programming language in a friendly, self-teaching, format. Practical analogies using real world applications are integrated throughout the text to explain technical concepts. The book includes a variety of end-of-chapter practice exercises, e.g., programming, theoretical, and multiple-choice.FEATURES:Covers data structure fundamentals using PythonNumerous tips, analogies, and practical applications enhance understanding of subjects under discussion"Frequently Asked Questions" integrated throughout the text clarify and explain conceptsIncludes a variety of end-of-chapter exercises, e.g., programming, theoretical, and multiple choice.
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Become a Python Data Analyst
Enhance your data analysis and predictive modeling skills using popular Python toolsKey FeaturesCover all fundamental libraries for operation and manipulation of Python for data analysisImplement real-world datasets to perform predictive analytics with PythonAccess modern data analysis techniques and detailed code with scikit-learn and SciPyBook DescriptionPython is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.By the end of this book, you will have hands-on experience performing data analysis with Python.What you will learnExplore important Python libraries and learn to install Anaconda distributionUnderstand the basics of NumPyProduce informative and useful visualizations for analyzing dataPerform common statistical calculationsBuild predictive models and understand the principles of predictive analyticsWho this book is forBecome a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book
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Andre Gorz and the Sartrean Legacy
A comprehensive and scholarly exploration of the personal and philosophical origins of André Gorz's work, this book includes a unique analysis of his early untranslated texts, as well as critical discussion of his relationship to the work of Husserl, Sartre, Merleau-Ponty, Marx and Habermas. Reassessing pivotal notions such as the 'lifeworld' and the 'subject', it argues that Gorz has pioneered a person-centred social theory in which the motive and meaning of social critique is firmly rooted in people's lived experience.
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WAIC and WBIC with Python Stan
Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory.The key features of this indispensable book include:A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise.100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension.A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians.Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented.A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting.Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!
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Learn Quantum Computing with Python and IBM Quantum
A step-by-step guide to working with programs that exploit quantum computing principles, with the help of IBM Quantum, Qiskit, and PythonKey FeaturesUnderstand the difference between classical computers and quantum computersWork with key quantum computational principles such as superposition, interference, and entanglement, and observe how they are leveraged on the IBM Quantum systemsRun your own quantum experiments and applications by integrating with Qiskit and PythonBook DescriptionThe IBM Quantum Platform was built to enable developers to learn the basics of quantum computing by providing access to high performant quantum computers and provide documentation and courses to help get up to speed with the latest features in quantum computing. Updated with new examples and changes to the platform, this edition begins with an introduction to the IBM Quantum Platform and the Quantum Information Science Kit (Qiskit) SDK. You will become well versed in the IBM Quantum Composer interface as well as running your quantum circuits on a real quantum computer. Along the way, you’ll learn some of the fundamental principles regarding quantum mechanics, qubits, quantum gates, quantum circuits, and the error mitigation techniques that are used to perform operations on qubits. As you build on your knowledge, you’ll understand the functionality of IBM Qiskit and the developer-focused resources so you can create your own quantum algorithms. You’ll learn how to monitor and optimize your quantum circuits. Lastly, you’ll look at the fundamental quantum algorithms and understand how they can be applied effectively. By the end of this quantum computing book, you'll know how to build quantum algorithms and will have gained a practical understanding of quantum computation that you can apply to your research or business. What you will learnGet familiar with the features within the IBM Quantum PlatformCreate and visualize quantum gates and circuitsOperate quantum gates on qubits using the IBM Quantum ComposerInstall and run your quantum circuits on an IBM Quantum computerDiscover Qiskit and its many features such as the Qiskit RuntimeGet to grips with fundamental quantum algorithms and error mitigation techniques to help you get startedUnderstand the new era of quantum utility and how this moves us closer towards quantum advantageWho this book is forThis book is for Python developers who are looking to learn quantum computing from the ground up and put their knowledge to use in practical situations with the help of the IBM Quantum Platform and Qiskit. Some background in computer science and high school-level physics and math is required.
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Dynamic System Modelling and Analysis with MATLAB and Python
Dynamic System Modeling & Analysis with MATLAB & Python A robust introduction to the advanced programming techniques and skills needed for control engineering In Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers, accomplished control engineer Dr. Jongrae Kim delivers an insightful and concise introduction to the advanced programming skills required by control engineers. The book discusses dynamic systems used by satellites, aircraft, autonomous robots, and biomolecular networks. Throughout the text, MATLAB and Python are used to consider various dynamic modeling theories and examples. The author covers a range of control topics, including attitude dynamics, attitude kinematics, autonomous vehicles, systems biology, optimal estimation, robustness analysis, and stochastic system. An accompanying website includes a solutions manual as well as MATLAB and Python example code. Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers provides readers with a sound starting point to learning programming in the engineering or biology domains. It also offers: A thorough introduction to attitude estimation and control, including attitude kinematics and sensors and extended Kalman filters for attitude estimationPractical discussions of autonomous vehicles mission planning, including unmanned aerial vehicle path planning and moving target trackingComprehensive explorations of biological network modeling, including bio-molecular networks and stochastic modelingIn-depth examinations of control algorithms using biomolecular networks, including implementation Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers is an indispensable resource for advanced undergraduate and graduate students seeking practical programming instruction for dynamic system modeling and analysis using control theory.
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