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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: Learning Devsecops: A Practical Guide to Processes and Tools
Автор: Steve Suehring
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 363
Язык: английский
Формат: pdf, epub
Размер: 11.2 MB

How do some organizations maintain 24-7 internet-scale operations? How can organizations integrate security while continuously deploying new features? How do organizations increase security within their DevOps processes? This practical guide helps you answer those questions and more. Author Steve Suehring provides unique content to help practitioners and leadership successfully implement DevOps and DevSecOps. Learning DevSecOps emphasizes prerequisites that lead to success through best practices and then takes you through some of the tools and software used by successful DevSecOps-enabled organizations. You'll learn how DevOps and DevSecOps can eliminate the walls that stand between development, operations, and security so that you can tackle the needs of other teams early in the development lifecycle. What is DevSecOps? It depends on who you ask. As defined in this book, DevSecOps is a set of agile and iterative practices that help to deliver software and technology systems rapidly, accurately, and repeatedly, emphasizing processes and people above tools. This book is for anyone interested in learning about DevSecOps and its predecessor, DevOps. You might be involved in development, operations, or security and want to learn about the melding of all three into a set of tools and processes for making production-level deployments easier. Being able to write code, commit and push the code, and have tests automatically executed on that code is one such practice in DevSecOps.

Название: Hands-On Differential Privacy: Introduction to the Theory and Practice Using OpenDP
Автор: Ethan Cowan, Michael Shoemate, Mayana Pereira
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 373
Язык: английский
Формат: epub
Размер: 10.1 MB

Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows. The OpenDP Library is part of the larger OpenDP Project. OpenDP is a community effort to build a trustworthy suite of open-source tools for enabling privacy-protective analysis of sensitive personal data, focused on a library of algorithms for generating differentially private statistical releases. The target use cases for OpenDP are to enable government, industry, and academic institutions to safely and confidently share sensitive data to support scientifically oriented research and exploration in the public interest. We aim for OpenDP to flexibly grow with the rapidly advancing science of differential privacy, and be a pathway to bring the newest algorithmic developments to a wide array of practitioners. SmartNoise is a collaboration between Microsoft, Harvard’s Institute for Quantitative Social Science (IQSS), and the OpenDP Project. The project aims to connect solutions from the research community with the lessons learned from real-world deployments to make differential privacy broadly accessible. Building upon the foundation of the OpenDP Library, the SmartNoise SDK includes two Python packages: smartnoise-sql and smartnoise-synth.

Название: Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs at Scale (Final Release)
Автор: James Phoenix, Mike Taylor
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 499
Язык: английский
Формат: epub
Размер: 33.5 MB

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. All of the code in this book is in Python and was designed to be run in a Jupyter Notebook or Google Colab notebook. The concepts taught in the book are transferable to jаvascript or any other coding language if preferred, though the primary focus of this book is on prompting techniques rather than traditional coding skills. The code can all be found on GitHub, and we will link to the relevant notebooks throughout. It’s highly recommended that you utilize the GitHub repository and run the provided examples while reading the book.

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.