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Название: ActivityPub: Programming for the Social Web (4th Early Release)
Автор: Evan Prodromou
Издательство: O’Reilly Media, Inc.
Год: 2024-05-02
Страниц: 327
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

ActivityPub is the new standard for connecting social networks together on the social web. This open, decentralized social networking protocol defines an API for sharing activities to a social network and a procedure that servers use to distribute those activities to a subscriber's feed. With this book, you'll learn how to assemble ActivityPub-enabled clients for making new kinds of social apps on top of existing networks and build ActivityPub servers that create new human or automated accounts on the social web. With those skills under your belt, you can explore other applications of this publish-subscribe technology: content management systems, internet of things, and enterprise automation. With hands-on examples and in-depth knowledge from Evan Prodromou, one of the authors of the protocol, this is the ActivityPub handbook that every social software hacker needs. The goal of this book is to teach you how to use the standards of the social web to make cool software and help human beings connect in fascinating ways. You can use social web technologies to connect an existing project to the social web, or you can start something new from scratch. You can make a tool that only a few people will use or something that changes how people live their lives, online and off. Let’s take a quick look at the main relevant standards for social web software: Activity Streams 2.0 for data, the ActivityPub federation protocol for server interconnection, and the ActivityPub API for client-to-server programming.

Название: Signal Processing with Python: A Practical Approach
Автор: Irshad Ahmad Ansari, Varun Bajaj
Издательство: IOP Publishing
Год: 2024
Страниц: 297
Язык: английский
Формат: pdf (true)
Размер: 59.5 MB

This book explores the domain of signal processing using Python, with the help of working examples and accompanying code. The book introduces the concepts of Python programming via signal processing with numerous hands-on examples and code snippets. The book will enable readers to appreciate the power of Python in this field and write their code to implement complex signal processing algorithms such as signal compression, cleaning, segmentation, decomposition, and feature extraction and be able to incorporate machine learning models using relevant Python libraries. This book aims to bring together professionals from academia and industry to ignite new developments and techniques in the domain of signal processing with Python. Python, being an open source and popular language, has attracted a lot of application development. Signal processing has also seen tremendous growth in the recent past. The majority of signal processing work revolves around simulation and testing before the final hardware implementation. Many times, code-based implementation itself is sufficient. These implementations make use of a cloud processing unit for a faster and more efficient user experience. Python has been developed as a tool for simulation, visualization, understanding, and manipulation of signals. We will use Python libraries MNE, NumPy, Matplotlib, and Pandas to preprocess and make data usable for further Machine Learning algorithms and models.

Название: Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs
Автор: Thomas W. MacFarland
Издательство: Springer
Год: 2024
Страниц: 536
Язык: английский
Формат: pdf (true)
Размер: 21.5 MB

Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term "Data Science" and discusses the many professional skills and competencies affiliated with the industry. With Data Science being a leading indicator of interest in STEM fields, the text also investigates this ongoing growth of demand in these spaces, with the goal of providing readers who are entering the professional world with foundational knowledge of required skills, job trends, and salary expectations. The text provides a historical overview of computing and the field's progression to R as it exists today, including the multitude of packages and functions associated with both Base R and the tidyverse ecosystem. Readers will learn how to use R to work with real data, as well as how to communicate results to external stakeholders. A distinguishing feature of this text is its emphasis on the emerging use of APIs to obtain data. This text was developed to assist beginning students and early stages researchers in their attempt to make sense of how software can be used in biostatistics, viewing an all-pervasive concept of biostatistics in the large and the many disciplines associated with biostatistics. To meet this challenge, R was selected as the most appropriate programming language, calling on Base R (e.g., the many functions made available when R is first downloaded) and supporting packages (e.g., the thousands of auxiliary R software collections that provide functionality far beyond what is available in Base R, especially packages associated with the Tidyverse ecosystem).

Название: Python Tkinter 35 Mini Projects : Practical guide for begineer
Автор: Vaishali B. Bhagat
Издательство: Independently published
Год: 2024
Страниц: 218
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Dive into the world of Python GUI programming with Tkinter through 35 exciting mini projects! Perfect for beginners and those looking to enhance their skills, this book offers a hands-on approach to learning. From creating simple interfaces to building interactive applications, each project is designed to help you grasp Tkinter concepts effortlessly. With clear explanations and practical examples, you'll gain confidence in GUI development while unleashing your creativity. Start your journey today and discover the power of Python Tkinter! Welcome to our Tkinter tutorial! In this program, we'll explore how to make a simple graphical user interface (GUI) using Python Tkinter. Our aim is to build a program that greets users with a welcome message when they click a button. To do this, we'll be using Tkinter, the most popular library for making GUIs in Python. We'll use two widgets: a Button labeled "Click Me" and a Label to display the text. Initially, the label won't show any text. However, when the button is clicked, it will update to show the message "Welcome to Python Programming world!" in a bold font. Let's dive in and create our first Tkinter app! Requirements: Text Editor or IDE: You'll need a text editor to write the code. I've used the Thonny IDE for writing and executing this program. However, you can use any Python IDE or text editor of your choice to run the program.

Название: Practical Python Backend Programming: Build Flask and FastAPI applications, asynchronous programming, containerization and deploy apps on cloud
Автор: Tim Peters
Издательство: GitforGits
Год: 2024
Страниц: 431
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

"Practical Python Backend Programming" is a quick pragmatic book that teaches both new and experienced developers the fundamentals of backend development with Python. All sorts of developers, from Python programmers to non-Python programmers, full stack developers, and web developers, will find what they need to know to become experts in backend programming in this entire book. The book covers key topics in backend development, including how to set up stable development environments and how to use virtual environments for better dependency management. With this book, readers will have a firm grasp of Python programming with an emphasis on backend tasks by learning the language's syntax, data structures, and functions. The book teaches you to create and launch dynamic web apps by providing an in-depth look at web frameworks such as Flask and FastAPI. It teaches SQLAlchemy for efficient data handling and advanced database integration, and it shows to improve applications with databases like PostgreSQL, MySQL, and MongoDB. Strategies for managing concurrent operations and improving performance are also covered in the book, along with asynchronous programming in Python. This book delves into various authentication methods, secure communication protocols such as HTTPS, and techniques to secure REST APIs. For efficient management of asynchronous tasks and real-time data processing, it also introduces message brokers such as RabbitMQ and Kafka. The book teaches its readers how to containerize apps and manage them on a large scale by integrating technologies like Docker and Kubernetes.

Название: Ethics, Machine Learning, and Python in Geospatial Analysis
Автор: Mohammad Gouse Galety, Arul Kumar Natarajan, Tesfaye Fufa Gedefa, Tsegaye Demsis Lemma
Издательство: IGI Global
Год: 2024
Страниц: 359
Язык: английский
Формат: pdf (true), epub
Размер: 26.8 MB

In geospatial analysis, navigating the complexities of data interpretation and analysis presents a formidable challenge. Traditional methods often need to efficiently handle vast volumes of geospatial data while providing insightful and actionable results. Scholars and practitioners grapple with manual or rule-based approaches, hindering progress in understanding and addressing pressing issues such as climate change, urbanization, and resource management. Ethics, Machine Learning, and Python in Geospatial Analysis offers a solution to the challenges faced by leveraging the extensive library support and user-friendly interface of Python and Machine Learning. The book's meticulously crafted chapters guide readers through the intricacies of Python programming and its application in geospatial analysis, from fundamental concepts to advanced techniques. The chapters within this book span a diverse range of topics, each meticulously crafted to provide readers with a holistic understanding of the subject matter. From examining the ethical dimensions of GIS data privacy to mastering geospatial analysis with Python, each Chapter contributes to a nuanced field exploration. Further, prominent Python libraries for geospatial analysis are explored. GeoPandas is introduced, detailing its capabilities in working with geospatial data, handling geometric data structures, and leveraging spatial operations. Shapely is examined for its role in geometric manipulations. Fiona is explored as a library for handling geospatial data.

Название: Fundamental Mathematical Concepts for Machine Learning in Science
Автор: Umberto Michelucci
Издательство: Springer
Год: 2024
Страниц: 259
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

This book is for individuals with a scientific background who aspire to apply Machine Learning (ML) within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine Learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating Machine Learning effectively into their research. Numerous texts delve into the technical execution of Machine Learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. Although the book does not delve into programming details, it points out its relevance in Machine Learning, especially considering Python.

Название: Data and Process Visualisation for Graphic Communication: A Hands-on Approach with Python
Автор: Francesco Bianconi
Издательство: Springer
Год: 2024
Страниц: 242
Язык: английский
Формат: pdf (true)
Размер: 20.1 MB

This book guides the reader through the process of graphic communication with a particular focus on representing data and processes. It considers a variety of common graphic communication scenarios among those that arise most frequently in practical applications. Graphic communication is a form of visual communication that employs images and graphics to convey information, ideas, and messages. It plays a fundamental role in several aspects of our personal and professional lives including education and learning, science and research, brainstorming, and decision-making as well as marketing and branding. This volume aims at guiding the reader through the process of graphic communication with particular focus on representing data and processes. It considers a variety of common graphic communication scenarios among those that arise most frequently in practical applications, e.g., representing magnitudes, proportions, relations, groups, geographical data, timelines, etc. At the time of writing Python is consistently ranked as the most popular programming language in the world. As for data visualization, there are already a number of packages available: Bokeh, HoloViz, Matplotlib, Plotly and Seaborn are just some examples. In writing this book we therefore had to made a choice about which package(s) to consider for the examples presented herein. Then, for each type of chart presented in the book, we proposed the implementation that we thought was the most clear and concise. When there were substantially equivalent solutions, we favored Matplotlib, both due to personal experience and the historical importance of this library.

Название: Introduction to Python: With Applications in Optimization, Image and Video Processing, and Machine Learning
Автор: David Báez-López, David Alfredo Báez Villegas
Издательство: CRC Press
Серия: The Python Series
Год: 2024
Страниц: 453
Язык: английский
Формат: pdf (true), epub
Размер: 11.1 MB

Introduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, Computer Science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool. The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book. Python is a high-level programming language that supports procedural, imperative, object-oriented, and functional modes. This book covers the first three modes. Python has a simple easy to learn syntax. Python supports modules, libraries, and packages, encouraging program modularity and code reuse. The book may be divided in two parts. The first part, comprised of Chapters 1 to 8, can be used by a person interested in learning Python from scratch; that is, he/she does not have any knowledge of Python. The learning goes from installing Python to implementing functions and generating complex plots. The second part covers advanced topics such as optimization, image and video processing, and Machine Learning using Keras, TensorFlow, and neural networks. Throughout the book, the use of external modules or libraries makes the Python programs very powerful.

Название: Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications
Автор: N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo
Издательство: Springer
Год: 2024
Страниц: 287
Язык: английский
Формат: pdf (true)
Размер: 11.5 MB

Focusing on the fundamentals of Machine Learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced Machine Learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect―each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials. We discuss the transformative impact of Deep Learning compared to classical approaches, which heavily rely on handcrafted features and hyperparameter tuning. To this extent, the chapter explores a range of advanced Deep Learning models, including Convolutional Neural Networks (CNNs) for materials image analysis, Long Short-Term Memory networks (LSTMs) for sequential materials data, Generative Adversarial Networks (GANs) for generating new material structures, Graph Neural Networks (GNNs) for analyzing materials graphs, Variational Autoencoders (VAEs) for materials representation learning, and Reinforcement Learning (RL) which has been widely used in materials domain. Each model is presented with a detailed explanation of its underlying principles, architectures, and training methodologies.