Search results for "master multi-paradigm python programming"
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Introduction to GIS Programming and Fundamentals with Python and ArcGIS®
Combining GIS concepts and fundamental spatial thinking methodology with real programming examples, this book introduces popular Python-based tools and their application to solving real-world problems. It elucidates the programming constructs of Python with its high-level toolkits and demonstrates its integration with ArcGIS Theory. Filled with hands-on computer exercises in a logical learning workflow this book promotes increased interactivity between instructors and students while also benefiting professionals in the field with vital knowledge to sharpen their programming skills. Readers receive expert guidance on modules, package management, and handling shapefile formats needed to build their own mini-GIS. Comprehensive and engaging commentary, robust contents, accompanying datasets, and classroom-tested exercises are all housed here to permit users to become competitive in the GIS/IT job market and industry.
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Exercises in Programming Style
The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as "The best programming book of the decade." This new edition retains the same presentation but has been upgraded to Python 3, and there is a new section on neural network styles.Using a simple computational task (term frequency) to illustrate different programming styles, Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs.The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, interactivity, and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style, next shows an example program, and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.
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Hands-On Bitcoin Programming with Python
Simplified Python programming for Bitcoin and blockchainKey FeaturesBuild Bitcoin applications in Python with the help of simple examplesMine Bitcoins, program Bitcoin-enabled APIs and transaction graphs, and build trading botsAnalyze Bitcoin transactions and produce visualizations using Python data analysis toolsBook DescriptionBitcoin is a cryptocurrency that’s changing the face of online payments. Hands-On Bitcoin Programming with Python teaches you to build software applications for mining and creating Bitcoins using Python.This book starts with the basics of both Bitcoin and blockchain and gives you an overview of these inherent concepts by showing you how to build Bitcoin-driven applications with Python. Packed with clear instructions and practical examples, you will learn to understand simple Python coding examples that work with this cryptocurrency.By the end of the book, you’ll be able to mine Bitcoins, accept Bitcoin payments on the app, and work with the basics of blockchain technology to create simply distributed ledgers.What you will learnMaster the Bitcoin APIs in Python to manipulate Bitcoin from your Python appsBuild your own Bitcoin trading bots to buy Bitcoins at a lower price and sell them at a higher priceWrite scripts to process Bitcoin payments through a website or appDevelop software for Bitcoin mining to create Bitcoin currency on your own computer hardwareCreate your own keys, addresses, and wallets in Python codeWrite software to analyze Bitcoin transactions and produce reports, graphs, and other visualizationsWho this book is forHands-On Bitcoin Programming with Python consists of examples that will teach you to build your own Bitcoin application. You will learn to write scripts, build software for mining, and create Bitcoins using Python. Anyone with prior Python experience, who wants to explore Python Bitcoin programming and start building Bitcoin-driven Python apps, will find this book useful.
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Learning Robotics Using Python
About This BookDesign, simulate, build and program an interactive autonomous mobile robotProgram Robot Operating System using PythonGet a grip on the hands-on guide to robotics for learning various robotics concepts and build an advanced robot from scratchWho This Book Is ForIf you are an engineer, a researcher, or a hobbyist, and you are interested in robotics and want to build your own robot, this book is for you. Readers are assumed to be new to robotics but should have experience with Python.
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Python Machine Learning Blueprints
Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and KerasKey FeaturesGet to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and KerasImplement advanced concepts and popular machine learning algorithms in real-world projectsBuild analytics, computer vision, and neural network projects Book DescriptionMachine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.What you will learnUnderstand the Python data science stack and commonly used algorithmsBuild a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window Understand NLP concepts by creating a custom news feedCreate applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forkedGain the skills to build a chatbot from scratch using PySparkDevelop a market-prediction app using stock dataDelve into advanced concepts such as computer vision, neural networks, and deep learningWho this book is forThis book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.
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Head First Programming
Looking for a reliable way to learn how to program on your own, without being overwhelmed by confusing concepts? "Head First Programming" introduces the core concepts of writing computer programs - variables, decisions, loops, functions, and objects - which apply regardless of the programming language. This book offers concrete examples and exercises in the dynamic and versatile Python language to demonstrate and reinforce these concepts. You can learn the basic tools to start writing the programs that interest you, and get a better understanding of what software can (and cannot) do. When you're finished, you'll have the necessary foundation to learn any programming language or tackle any software project you choose.With a focus on programming concepts, this book teaches you how to: understand the core features of all programming languages, including variables, statements, decisions, loops, expressions, and operators; reuse code with functions; use library code to save time and effort; select the best data structure to manage complex data; write programs that talk to the Web; share your data with other programs; and, write programs that test themselves and help you avoid embarrassing coding errors. We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, "Head First Programming" uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.
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Programming Multi-Agent Systems
These are the post-proceedings of the International Workshop on Programming Multi-Agent Systems (ProMAS 2007), the ?fth of a series of workshops that is attracting increasing attention from researchersand practitioners in multi-agent systems. Multi-agent systems (MAS) constitute a promising software development paradigm for complex and distributed applications. The aim of the ProMAS workshop series is to promote and contribute to the establishment of MAS as a mainstream approach to the development of industrial-strength software. In particular, ProMAS aims to address the technologies that are required for - plementing multi-agentsystems designs or speci?cations e?ectively. We promote the discussion and exchangeof ideas on principles, concepts, requirements, te- niques,andtoolsthatareessentialforprogrammingapproachesandtechnologies speci?cally devised for MAS. Theidea oforganizingthe ?rstworkshopofthe series was?rstdiscussedd- ing the Dagstuhl seminar "ProgrammingMulti-Agent Systems Based onLogic", where the focus was on logic-based approaches. It was felt that the scope should be broadened beyond logic-based approaches, thus giving the current scope and aims of ProMAS.After four very successful editions of the ProMAS workshop series, which took place at AAMAS 2003 (Melbourne, Australia), AAMAS 2004 (New York, USA), AAMAS 2005(Utrecht,The Netherlands), and AAMAS 2006(Hakodate, Japan), the ?fth edition took place on May 14 in Honolulu, Hawai'i, in c- junction with AAMAS 2007, the main international conference on autonomous agents and MAS. ProMAS 2007 received 17 submissions. These were reviewed by members of the Program Committee, and 11 papers were accepted.
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Programming PyTorch for Deep Learning
Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks.Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.Learn how to deploy deep learning models to productionExplore PyTorch use cases from several leading companiesLearn how to apply transfer learning to imagesApply cutting-edge NLP techniques using a model trained on WikipediaUse PyTorch’s torchaudio library to classify audio data with a convolutional-based modelDebug PyTorch models using TensorBoard and flame graphsDeploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud
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