Programowanie
Yoni Ramaswami, Dael Williamson, Jan Govaere
Written by Databricks Senior Solutions Architect Yoni Ramaswami, whose expertise in Data and AI has shaped innovative digital transformations across industries, this comprehensive guide bridges foundational concepts of time series analysis with the Spark framework and Databricks, preparing you to tackle real-world challenges with confidence.From preparing and processing large-scale time series datasets to building reliable models, this book offers practical techniques that scale effortlessly for big data environments. You’ll explore advanced topics such as scaling your analyses, deploying time series models into production, Generative AI, and leveraging Spark's latest features for cutting-edge applications across industries. Packed with hands-on examples and industry-relevant use cases, this guide is perfect for data engineers, ML engineers, data scientists, and analysts looking to enhance their expertise in handling large-scale time series data.By the end of this book, you’ll have mastered the skills to design and deploy robust, scalable time series models tailored to your unique project needs—qualifying you to excel in the rapidly evolving world of big data analytics.*Email sign-up and proof of purchase required
Time Series with PyTorch. Modern Deep Learning Toolkit for Real-World Forecasting Challenges
Graeme Davidson, Lei Ma
Neural networks are powerful tools for time-series forecasting, but applying them effectively requires both practical experience and a clear understanding of architectures, training strategies, and evaluation methods. This book brings these ideas together in a structured and practical way.Starting with PyTorch fundamentals, you will build neural networks from scratch and progress through recurrent networks, attention mechanisms, and transformers before exploring forecasting architectures such as N-BEATS, N-HiTS, and the Temporal Fusion Transformer. Along the way, you will learn robust hyperparameter tuning, conformal prediction for uncertainty estimation, and reliable evaluation practices.Unlike most forecasting books, this text also explores topics often overlooked or treated separately, including transfer learning across collections of series, synthetic data generation with diffusion models, and self-supervised representation learning. Beyond forecasting, later chapters cover classification, clustering, anomaly detection, and embeddings for large-scale time-series modeling.Throughout, the focus is pragmatic: theory is reinforced through experimentation and implementation so you can apply these methods confidently to real-world time-series problems.
Bhaskar Chaudhary
Tkinter is the built-in GUI package that comes with standard Python distributions. It is a cross-platform package, which means you build once and deploy everywhere. It is simple to use and intuitive in nature, making it suitable for programmers and non-programmers alike.This book will help you master the art of GUI programming. It delivers the bigger picture of GUI programming by building real-world, productive, and fun applications such as a text editor, drum machine, game of chess, audio player, drawing application, piano tutor, chat application, screen saver, port scanner, and much more. In every project, you will build on the skills acquired in the previous project and gain more expertise. You will learn to write multithreaded programs, network programs, database-driven programs, asyncio based programming and more. You will also get to know the modern best practices involved in writing GUI apps. With its rich source of sample code, you can build upon the knowledge gained with this book and use it in your own projects in the discipline of your choice.
Alejandro Rodas de Paz
As one of the more versatile programming languages, Python is well-known for its batteries-included philosophy, which includes a rich set of modules in its standard library; Tkinter is the library included for building desktop applications. Due to this, Tkinter is a common choice for rapid GUI development, and more complex applications can benefit from the full capabilities of this library. This book covers all of your Tkinter and Python GUI development problems and solutions.Tkinter GUI Application Development Cookbook starts with an overview of Tkinter classes and at the same time provides recipes for basic topics, such as layout patterns and event handling. Next, we cover how to develop common GUI patterns, such as entering and saving data, navigating through menus and dialogs, and performing long-running actions in the background.You can then make your apps leverage network resources effectively and perform graphical operations on a canvas and related tasks such as detecting collisions between items. Finally, this book covers using themed widgets, an extension of Tk widgets that have a more native look and feel. Finally, this book covers using the canvas and themed widgets.By the end of the book, you will have an in-depth knowledge of Tkinter classes, and will know how to use them to build efficient and rich GUI applications.
David Love
Tkinter is a modular, cross-platform application development toolkit for Python. When developing GUI-rich applications, the most important choices are which programming language(s) and which GUI framework to use. Python and Tkinter prove to be a great combination. This book will get you familiar with Tkinter by having you create fun and interactive projects. These projects have varying degrees of complexity. We'll start with a simple project, where you'll learn the fundamentals of GUI programming and the basics of working with a Tkinter application. After getting the basics right, we'll move on to creating a project of slightly increased complexity, such as a highly customizable Python editor. In the next project, we'll crank up the complexity level to create an instant messaging app. Toward the end, we'll discuss various ways of packaging our applications so that they can be shared and installed on other machines without the user having to learn how to install and run Python programs.
Mark J. Price
.NET is more powerful—and more complex—than ever. As the ecosystem evolves, so do the expectations from developers. Mastering the tools and patterns of professional .NET development can be the difference between simply shipping code and delivering resilient, scalable software. Tools and Skills for .NET 10 is a practical guide to leveling up in today’s .NET world, offering both hard technical skills and career strategies to help you thrive.Inside, you’ll learn how to manage codebases with Git and implement debugging and memory analysis techniques that save hours of guesswork. The book also covers best practices for writing and maintaining documentation for your code, services, and APIs, which is crucial for team collaboration. You’ll explore cryptography for securing data and concurrency for building responsive apps. Further chapters explore dependency injection, testing for performance and reliability, and packaging your apps in Docker containers.By the end of this book, you’ll be equipped not only with technical skills but also with the confidence to apply them in real environments. Whether you're aiming for a promotion, switching jobs, or preparing for technical interviews, you'll have the skills and the language to present yourself as a capable, well-rounded .NET developer ready for anything the industry throws at you.
Mark J. Price
.NET is more powerful—and more complex—than ever. As the ecosystem evolves, so do the expectations from developers. Mastering the tools and patterns of professional .NET development can be the difference between simply shipping code and delivering resilient, scalable software. Tools and Skills for .NET 10 is a practical guide to leveling up in today’s .NET world, offering both hard technical skills and career strategies to help you thrive.Inside, you’ll learn how to manage codebases with Git and implement debugging and memory analysis techniques that save hours of guesswork. The book also covers best practices for writing and maintaining documentation for your code, services, and APIs, which is crucial for team collaboration. You’ll explore cryptography for securing data and concurrency for building responsive apps. Further chapters explore dependency injection, testing for performance and reliability, and packaging your apps in Docker containers.By the end of this book, you’ll be equipped not only with technical skills but also with the confidence to apply them in real environments. Whether you're aiming for a promotion, switching jobs, or preparing for technical interviews, you'll have the skills and the language to present yourself as a capable, well-rounded .NET developer ready for anything the industry throws at you.
DAVID WYAND
Torque 3D is a popular game engine that supports you in every step along the way to making your game a reality. Even with all of the power and tools that Torque 3D provides, finishing a high quality 3D game requires time and knowledge.Torque 3D Game Development Cookbook is a practical guide that takes you through each of the major steps on the journey to creating your game, while learning a few tricks along the way.The recipes in this book start off with learning some of the finer points about TorqueScript. The book then moves on to each of Torque 3D's subsystems and ends with a variety of game play recipes.The various topics covered include activating level-specific game code and scheduling game events, dragging and dropping items between windows to work with an in-game inventory system, and covering the seams between objects with well placed decals. Some of the advanced topics include writing custom shaders and postFX, using zones to improve rendering performance, and enhancing your game's ambience through sound.Once you are done with Torque 3D Game Development Cookbook you'll be on your way to creating amazing 3D games and gain expert knowledge of Torque 3D.
Curtis Miller
Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics.You’ll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them.By the end of this book, you will have the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.