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Python & AI For Dummies
Unlock the combined power of Python and AI to supercharge your next technical project and your career Python & AI For Dummies explores the exciting combo of two revolutionary technologies that are reshaping how we work, code, and solve problems. Authors John C. Shovic and Mary Everett draw on their extensive work in computer science, precision agriculture, and artificial intelligence to walk you through the intersection of Python programming and AI applications. No matter your professional background, learn how to use Python's versatility to unlock AI's transformative power. This guide offers hands-on approaches to implementing AI with Python. It moves from foundational concepts to advanced applications, covering essential Python libraries: TensorFlow, PyTorch, OpenCV, and scikit-learn. It also explores essential AI concepts, like neural networks, machine learning models, and natural language processing. Python & AI For Dummies is packed with hands-on projects and real-world examples, that explain how to build machine vision systems, create language models, implement reinforcement learning, and develop AI agents. Broken down into manageable, jargon-free lessons, explore topics from theoretical programming fundamentals to cutting-edge AI applications. Inside the book: Grasp fundamental AI concepts including machine learning, neural networks, and deep learning architecturesExplore eight essential Python libraries designed specifically for AI development and data manipulationBuild practical projects, including machine vision systems, large language models, and AI agentsLearn proven techniques for data preprocessing, model training, and avoiding common pitfalls like overfittingGain insights into emerging technologies including generative AI, transformers, and the Model Context Protocol A can’t-miss resource for programmers interested in expanding their AI skillset, professionals in finance or marketing who want to develop new capabilities, and small business owners ready to streamline their operations, Python & AI For Dummies offers step-by-step guidance, troubleshooting tips, and real-world applications in industries from manufacturing to agriculture.
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Python Programming for Biology
Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.
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Expert Python Programming -
Become an ace Python programmer by learning best coding practices and advance-level concepts with Python 3.5About This Book• Based on the latest stable version of Python (version 3.5)• Creating well manageable code that will run in various environments with different sets of dependencies• Packed with advanced concepts and best practices to write efficient Python codeWho This Book Is ForThe book would appeal to web developers and Python programmers who want to start using version 3.5 and write code efficiently. Basic knowledge of Python programming is expected.What You Will Learn• Conventions and best practices that are widely adopted in the python community• Package python code effectively for community and production use• Easy and lightweight ways to automate code deployment on remote systems• Improve your code's quality, reliability, and performance• Write concurrent code in python• Extend python with code written in different languagesIn DetailPython is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is a challenge.The focus of the book is to familiarize you with common conventions, best practices, useful tools and standards used by python professionals on a daily basis when working with code.You will begin with knowing new features in Python 3.5 and quick tricks for improving productivity. Next, you will learn advanced and useful python syntax elements brought to this new version. Using advanced object-oriented concepts and mechanisms available in python, you will learn different approaches to implement metaprogramming. You will learn to choose good names, write packages, and create standalone executables easily.You will also be using some powerful tools such as buildout and vitualenv to release and deploy the code on remote servers for production use. Moving on, you will learn to effectively create Python extensions with C, C++, cython, and pyrex. The important factors while writing code such as code management tools, writing clear documentation, and test-driven development are also covered.You will now dive deeper to make your code efficient with general rules of optimization, strategies for finding bottlenecks, and selected tools for application optimization.By the end of the book, you will be an expert in writing efficient and maintainable code.Style and approach An easy-to-follow guide that covers industry followed best practices in Python programming
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Physik mit Python
Dieses Lehrbuch führt Sie anhand von physikalischen Fragestellungen aus der Mechanik in die Programmiersprache Python ein. Neben der reinen Simulation von physikalischen Systemen wird besonderes Augenmerk auf die Visualisierung von Ergebnissen und das Erstellen von Animationen gelegt. Mit zahlreichen Beispielen und Übungsaufgaben ermöglicht dieses Buch so den praktischen Einstieg in das wissenschaftliche Rechnen.Sie lernen Kurvenanpassungen durchzuführen sowie lineare und nicht-lineare Gleichungssysteme zu lösen, die bei der Behandlung von statischen Problemen auftreten. Auch die Lösung von Differentialgleichungen, die dynamische Systeme beschreiben, sowie Themen wie Fourier-Transformationen und Eigenwertprobleme kommen nicht zu kurz. Mithilfe der in diesem Buch vorgestellten Simulationsbeispiele vertiefen Sie darüber hinaus Ihr Verständnis der zugrundeliegenden Physik, indem Sie die physikalischen Gesetze algorithmisch umsetzen und physikalische Aufgaben simulieren, die weitaus komplexer sind als die üblichen mit Papier und Bleistift lösbaren Aufgaben.Alle im Buch vorgestellten Programme, die fertigen Animationen sowie die Lösungen zu den Übungsaufgaben werden online bereitgestellt.Die vorliegende zweite Auflage enthält zahlreiche Ergänzungen, Korrekturen und Anpassungen an die aktuellen Versionen von Python, NumPy, SciPy und Matplotlib. Darüber hinaus erwartet Sie ein zusätzliches Kapitel über objektorientierte Programmiermethoden. Ob Sie also Physik oder eine Ingenieurwissenschaft mit hohem physikalischem Anteil studieren, oder ob Sie unterrichten und Ihre Lehre durch Simulationen und Animationen anreichern möchten – dieses Buch ist dabei Ihr optimaler Begleiter!
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Learning Advanced Python by Studying Open Source Projects
This book is one of its own kind. It is not an encyclopedia or a hands-on tutorial that traps readers in the tutorial-hell. It is a distillation of just one common Python user’s learning experience. The experience is packaged with exceptional teaching techniques, careful dependence unraveling and most importantly, passion.Learning Advanced Python by Studying Open Source Projects helps readers overcome the difficulty in their day-to-day tasks and seek insights from solutions in famous open source projects. Different from a technical manual, this book mixes the technical knowledge, real-world applications and more theoretical content, providing readers with a practical and engaging approach to learning Python.Throughout the book, readers will learn how to write Python code that is efficient, readable, and maintainable, covering key topics such as data structures, algorithms, object-oriented programming, and more. The author's passion for Python shines through in the book, making it an enjoyable and inspiring read for both beginners and experienced programmers.
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Python
This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1959.
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Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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Python Audio Cookbook
The Python Audio Cookbook offers an introduction to Python for sound and multimedia applications, with chapters that cover writing your first Python programs, controlling Pyo with physical computing, and writing your own GUI, among many others.Guiding the reader through a variety of audio synthesis techniques, the book empowers readers to combine their projects with popular platforms, from the Arduino to Twitter, and state-of-the-art practices such as AI. The Python Audio Cookbook balances accessible explanations for theoretical concepts, including Python syntax, audio processing and machine learning, with practical applications. This book is an essential introductory guide to Python for sound and multimedia practitioners, as well as programmers interested in audio applications.
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Introduction to Programming Concepts with Case Studies in Python
The current text provides a clear introduction to Computer Science concepts in a programming environment. It is designed as suitable use in freshman- or introductory level coursework in CS and provides the fundamental concepts as well as abstract theorems for solving computational problems. The Python language serves as a medium for illustrating and demonstrating the concepts.
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Beginning Python
Gain a fundamental understanding of Python’s syntax and features with this revised introductory and practical reference. Covering a wide array of Python–related programming topics, including addressing language internals, database integration, network programming, and web services, you’ll be guided by sound development principles. Updated to reflect the latest in Python programming paradigms and several of the most crucial features found in Python 3, Beginning Python, Fourth Edition also covers advanced topics such as extending Python and packaging/distributing Python applications. Ten accompanying projects will ensure you can get your hands dirty in no time. You will: Become a proficient Python programmer by following along with a friendly, practical guide to the language’s key featuresWrite code faster by learning how to take advantage of advanced features such as magic methods, exceptions, and abstractionGain insight into modern Python programming paradigms including testing, documentation, packaging, and distributionWork through several interesting projects, including a P2P file–sharing application, chat client, video game, remote text editor, and more Who This Book Is For Programmers, novice and otherwise, seeking a comprehensive introduction to the Python programming language.
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Mastering Python
Use advanced features of Python to write high-quality, readable code and packagesKey FeaturesExtensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive PythonShape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessingLearn how advanced Python features fit together to produce maintainable codeBook DescriptionEven if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challengesWhat you will learnWrite beautiful Pythonic code and avoid common Python coding mistakesApply the power of decorators, generators, coroutines, and metaclassesUse different testing systems like pytest, unittest, and doctestTrack and optimize application performance for both memory and CPU usageDebug your applications with PDB, Werkzeug, and faulthandlerImprove your performance through asyncio, multiprocessing, and distributed computingExplore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learnExtend Python's capabilities with C/C++ libraries and system callsWho this book is forThis book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary
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Expert Python Programming
Gain a deep understanding of building, maintaining, packaging, and shipping robust Python applicationsKey FeaturesDiscover the new features of Python, such as dictionary merge, the zoneinfo module, and structural pattern matchingCreate manageable code to run in various environments with different sets of dependenciesImplement effective Python data structures and algorithms to write, test, and optimize codeBook DescriptionThis new edition of Expert Python Programming provides you with a thorough understanding of the process of building and maintaining Python apps. Complete with best practices, useful tools, and standards implemented by professional Python developers, this fourth edition has been extensively updated. Throughout this book, you’ll get acquainted with the latest Python improvements, syntax elements, and interesting tools to boost your development efficiency.The initial few chapters will allow experienced programmers coming from different languages to transition to the Python ecosystem. You will explore common software design patterns and various programming methodologies, such as event-driven programming, concurrency, and metaprogramming. You will also go through complex code examples and try to solve meaningful problems by bridging Python with C and C++, writing extensions that benefit from the strengths of multiple languages. Finally, you will understand the complete lifetime of any application after it goes live, including packaging and testing automation.By the end of this book, you will have gained actionable Python programming insights that will help you effectively solve challenging problems.What you will learnExplore modern ways of setting up repeatable and consistent Python development environmentsEffectively package Python code for community and production useLearn modern syntax elements of Python programming, such as f-strings, enums, and lambda functionsDemystify metaprogramming in Python with metaclassesWrite concurrent code in PythonExtend and integrate Python with code written in C and C++Who this book is forThe Python programming book is intended for expert programmers who want to learn Python’s advanced-level concepts and latest features.Anyone who has basic Python skills should be able to follow the content of the book, although it might require some additional effort from less experienced programmers. It should also be a good introduction to Python 3.9 for those who are still a bit behind and continue to use other older versions.
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