Search results for "Python in Depth"
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R 611.00
Learning Robotics using Python
Design, simulate, and program interactive robots Key Features Design, simulate, build, and program an interactive autonomous mobile robot Leverage the power of ROS, Gazebo, and Python to enhance your robotic skills A hands-on guide to creating an autonomous mobile robot with the help of ROS and PythonBook DescriptionRobot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implement different capabilities in a robot without implementing them from scratch.This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you'll learn about robot modeling and how to design and simulate it using ROS. Moving on, we'll design robot hardware and interfacing actuators. Then, you'll learn to configure and program depth sensors and LIDARs using ROS. Finally, you'll create a GUI for your robot using the Qt framework. By the end of this tutorial, you'll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package.What you will learn Design a differential robot from scratch Model a differential robot using ROS and URDF Simulate a differential robot using ROS and Gazebo Design robot hardware electronics Interface robot actuators with embedded boards Explore the interfacing of different 3D depth cameras in ROS Implement autonomous navigation in ChefBot Create a GUI for robot controlWho this book is forThis book is for those who are conducting research in mobile robotics and autonomous navigation. As well as the robotics research domain, this book is also for the robot hobbyist community. You’re expected to have a basic understanding of Linux commands and Python.
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Quantitative Risk Management Using Python
Gain an understanding of various financial risks, the benefits of portfolio diversification, and the fundamental trade-off between risk and return. This book takes an in-depth journey into the world of quantitative risk management using Python, focusing on credit and market risk, with an extension to model risk. You'll start by reviewing the different types of financial risk, the benefit of diversification in a portfolio, and the fundamental trade-off between risk and return. The book then offers an in-depth look at managing credit and market risk in today's dynamic markets, all with practical Python implementations. Moving on, you'll examine common hedging strategies used to manage investment positions, along with practical implementations on evaluating risk-adjusted, as well as downside risk measures. Finally, you'll be introduced to common risks related to the development and use of machine learning models in finance. Whether you're a finance professional, academic, or student, Quantitative Risk Management Using Python will empower you to make informed decisions in today's complex financial landscape. What You Will Learn Explore techniques to assess and manage the risk of default by borrowers or counterparties. Identify, measure, and mitigate risks arising from fluctuations in market prices. Understand how derivatives can be employed for risk management purposes.
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R 580.00
Artificial Intelligence Programming with Python
A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.
R 958.00
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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R 641.00
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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Python in Excel Step-by-Step
An intuitive guide for professionals wanting to prepare for the future of Microsoft Excel by building Python in Excel skills and unleashing the power of their data. A hands-on guide to the foundational Python in Excel skills you’ll need to understand and use this powerful analytics tool, Python in Excel Step-by-Step is for current Excel users interested in expanding their data analysis skillset with Python. Analytics educator and Microsoft Excel MVP David Langer demonstrates how to use Python in Excel, tounlock new analytics capabilities in Excel, and build your foundation for the future of Excel: do-it-yourself (DIY) data science. The book leverages your existing Excel knowledge to learn the Python foundation you can apply right away. This is the same approach David has used to successfully teach more than 1,000 professionals Python – even if you’ve never written code before. David also includes: Targeted coverage of the Python fundamentals required for analytics – learn just what you need fastHow to use the powerful pandas and plotnine libraries to facilitate data manipulation and visualization using Python in ExcelA DIY data science roadmap for you to build the skills you need to unleash the power of your data to have more impact at work Perfect for professionals use Microsoft Excel for data analysis, like marketing managers, financial analysts, and supply chain manager, Python in Excel Step-by-Step is an invaluable new resource for all business professionals who use Excel and want to build skills for Excel’s AI-powered future.
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Deep Learning in Gaming and Animations
Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges.This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation.It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.
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Data Science with Python
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook DescriptionData Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is forData Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
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Python Programming on WIN32
Python is growing in popularity; based on download statistics, there are now over 450,000 people using Python, and more than 150,000 people using Python on Windows. Use of the language has been growing at about 40% per year since 1995, and there is every reason to believe that growth will continue. Despite Python's increasing popularity on Windows, Python Programming on Win32 is the first book to demonstrate how to use it as a serious Windows development and administration tool. Unlike scripting on Unix, Windows scripting involves integrating a number of components, such as COM or the various mail and database APIs, with the Win32 programming interface. While experienced Windows C++ programmers can find their way through the various objects, most people need some guidance, and this book is it. It addresses all the basic technologies for common integration tasks on Windows, explaining both the Windows issues and the Python code you need to glue things together.Topics include: * The Python language and the PythonWin extensions * Building a GUI with COM * Adding a Macro language * Distributing the application * Client-side COM for output and data access * Integration with mail and other internet protocols * Managing users and drives This is a vital and unique book. Python Programming on Win32 is an excellent presentation of Windows application development and a solid illustration of how to use Python in the Windows environment.
R 2,076.00
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Introduction to Python Programming and Data Structures, Global Edition
For introductory courses in Python Programming and Data Structures. A fundamentals first approach to programming helps students create efficient, elegant code. Introduction to Python Programming and Data Structures introduces students to basic programming concepts using a fundamentals-first approach that prepares students to learn object-oriented programming and advanced Python programming. This approach presents programming concepts and techniques that include control statements, loops, functions, and arrays before designing custom classes. Students learn basic logic and programming concepts prior to moving into object-oriented and GUI programming. The content incorporates a wide variety of problems with various levels of difficulty and covers many application areas to engage and motivate students.
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Python for Signal Processing
This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to “experiment and learn” as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.
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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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