Deep Learning for Time Series Cookbook. Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

Vitor Cerqueira, Luís Roque

Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions.By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.

Hands-On Azure Digital Twins. A practical guide to building distributed IoT solutions

Alexander Meijers

In today’s world, clients are using more and more IoT sensors to monitor their business processes and assets. Think about collecting information such as pressure in an engine, the temperature, or a light switch being turned on or off in a room. The data collected can be used to create smart solutions for predicting future trends, creating simulations, and drawing insights using visualizations. This makes it beneficial for organizations to make digital twins, which are digital replicas of the real environment, to support these smart solutions.This book will help you understand the concept of digital twins and how it can be implemented using an Azure service called Azure Digital Twins. Starting with the requirements and installation of the Azure Digital Twins service, the book will explain the definition language used for modeling digital twins. From there, you'll go through each step of building digital twins using Azure Digital Twins and learn about the different SDKs and APIs and how to use them with several Azure services. Finally, you'll learn how digital twins can be used in practice with the help of several real-world scenarios.By the end of this book, you'll be confident in building and designing digital twins and integrating them with various Azure services.

Kickstart Modern Android Development with Jetpack and Kotlin. Enhance your applications by integrating Jetpack and applying modern app architectural concepts

Catalin Ghita

With Jetpack libraries, you can build and design high-quality, robust Android apps that have an improved architecture and work consistently across different versions and devices. This book will help you understand how Jetpack allows developers to follow best practices and architectural patterns when building Android apps while also eliminating boilerplate code.Developers working with Android and Kotlin will be able to put their knowledge to work with this condensed practical guide to building apps with the most popular Jetpack libraries, including Jetpack Compose, ViewModel, Hilt, Room, Paging, Lifecycle, and Navigation. You'll get to grips with relevant libraries and architectural patterns, including popular libraries in the Android ecosystem such as Retrofit, Coroutines, and Flow while building modern applications with real-world data.By the end of this Android app development book, you'll have learned how to leverage Jetpack libraries and your knowledge of architectural concepts for building, designing, and testing robust Android applications for various use cases.

Banana Pi Cookbook. Over 25 recipes to build projects and applications for multiple platforms with Banana Pi

Ryad El-Dajani

The Art of Micro Frontends. Build highly scalable, distributed web applications with multiple teams - Second Edition

Florian Rappl, Lothar Schöttner

The organizational pattern of micro frontends allows you to split web applications into multiple parts, where each part can be owned by an autonomous team. Each team can have its own development and deployment life cycle, allowing every part of an application to be shipped in isolation. Following the strategies outlined in this book, you can avoid complexity while increasing resilience for your frontend. This updated second edition will guide you through the patterns available to implement a micro frontend solution. You’ll learn about micro frontends, the different architecture styles, and their areas of use. Then, you’ll learn how to prepare teams for the change to micro frontends, as well as how to adjust the user interface (UI) design and your CSS styles for scalability. Starting with the simplest variants of micro frontend architectures, the book progresses from static approaches to fully dynamic solutions that allow maximum scalability with faster release cycles. In the concluding chapters, you’ll strengthen the security level of your micro frontend solution, while reinforcing your overall knowledge with real-world case studies relating to micro frontends. By the end of this book, you’ll be able to decide whether and how micro frontends should be implemented to achieve high scalability for your web app.

Julia 1.0 Programming Complete Reference Guide. Discover Julia, a high-performance language for technical computing

Ivo Balbaert, Adrian Salceanu

Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI).You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs.Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system.By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications.This Learning Path includes content from the following Packt products:• Julia 1.0 Programming - Second Edition by Ivo Balbaert• Julia Programming Projects by Adrian Salceanu

Learn T-SQL Querying. A guide to developing efficient and elegant T-SQL code - Second Edition

Pedro Lopes, Pam Lahoud

Data professionals seeking to excel in Transact-SQL for Microsoft SQL Server and Azure SQL Database often lack comprehensive resources. Learn T-SQL Querying second edition focuses on indexing queries and crafting elegant T-SQL code enabling data professionals gain mastery in modern SQL Server versions (2022) and Azure SQL Database. The book covers new topics like logical statement processing flow, data access using indexes, and best practices for tuning T-SQL queries.Starting with query processing fundamentals, the book lays a foundation for writing performant T-SQL queries. You’ll explore the mechanics of the Query Optimizer and Query Execution Plans, learning to analyze execution plans for insights into current performance and scalability. Using dynamic management views (DMVs) and dynamic management functions (DMFs), you’ll build diagnostic queries. The book covers indexing and delves into SQL Server’s built-in tools to expedite resolution of T-SQL query performance and scalability issues. Hands-on examples will guide you to avoid UDF pitfalls and understand features like predicate SARGability, Query Store, and Query Tuning Assistant. By the end of this book, you‘ll have developed the ability to identify query performance bottlenecks, recognize anti-patterns, and avoid pitfalls

Hands-On Gradient Boosting with XGBoost and scikit-learn. Perform accessible machine learning and extreme gradient boosting with Python

Corey Wade, Kevin Glynn

XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You’ll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You’ll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you’ll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines.By the end of the book, you’ll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.