Verleger: 8
Max Brand
Les Burchard owned the local gambling palace, half the town, and most of the surrounding territory, and Walt Devons thousand-acre ranch would make him king of the land. The trouble was, Devon didnt want to sell. In a ruthless bid to claim the spread, Burchard tried everything from poker to murder. But Walt Devon was a betting man by nature, even when the stakes were his life. The way Devon figured, the odds were stacked against him. So he could either die alone... or takes his enemy to the grave with him. Max Brand at his best pure Western adventure! One rancher defends his land against those who want it by any means possible.
Wiktoria "Dalva" Jakubowska
Aurora Freeman to obiecująca młoda lekkoatletka. Dzięki stypendium sportowemu rozpoczyna trzecią klasę liceum w najbardziej prestiżowej szkole w Kalifornii. Jak sobie poradzi w tym środowisku, wśród pewnych siebie, dobrze sytuowanych dzieciaków? Po tym, jak zupełnym przypadkiem zadziera ze sławną na całe miasto czwórką wpływowych młodych mężczyzn, nie jest pewna, czy zdoła się odnaleźć w nowej rzeczywistości. Choć dziewczyna próbuje unikać kłopotów i zakopać topór wojenny, lider Stowarzyszenia TIME, Timothy Jang, nie potrafi zrezygnować z intryg, podstępów i... wprowadzania komplikacji do życia Aurory. Jedno jest pewne: tajemniczej grupy z Lowell High School nie można ot tak wyrzucić z pamięci ani pozbyć się z życia. Każda spędzona z nimi chwila zbliża Aurorę do ciemności, przed którą od dawna próbuje uciec. Czy nastolatka zdoła zaakceptować zepsute wnętrze Timothy'ego? A może między nimi stanie ktoś jeszcze? Jak elitarny świat i odkrycie jego tajemnic wpłyną na życie przeciętnej nastolatki? Czy ktokolwiek zdoła uratować Aurorę przed konsekwencjami ryzykownych decyzji i uchroni jej wrażliwą duszę
Wiktoria "Dalva" Jakubowska
Aurora Freeman nie wie, w którą stronę powinna pobiec. Została przygnieciona lawiną wiadomych i niewiadomych. Wszystkim, czego nie była w stanie przewidzieć. A tylko prawda może przynieść upragniony spokój Po przyjęciu urodzinowym Emily Hutson Aurora musi wielokrotnie stawiać czoła mrokowi. Na kolejne próby wystawiają ją nie tylko członkowie Stowarzyszenia TIME, ale również jej własne demony i Clarissa Jang. Jednak dziewczyna ma nadzieję, że pokonanie przeszkód pomoże jej odkryć prawdę o Timothym Jangu, o sobie i o kruchym świecie, w którym jeden niewłaściwy ruch może zaważyć na przyszłości. Choć panicznie się boi i nie wie, dokąd zaprowadzą ją powracające wspomnienia, jednego jest pewna: dzięki przyjaciołom nieco łatwiej przejść przez trudy życia. Szczególna zasługa przypada Timothyemu, który postanawia być dla Aurory murem obronnym. Tyle że każdą ścianę można zburzyć. Szczególnie jeśli zniszczeń będą chcieli dokonać ludzie, którzy są bezkarni i nie cofną się przed niczym. Czy istnieje właściwy tor biegu w mieście, w którym panuje znieczulica, a o wielu sprawach decydują koneksje? A może pora zrezygnować z pogoni za marzeniami i życiem wśród elit San Francisco? Bo chociaż czas kłamstw dobiegł końca, prawda wcale nie jest bezpieczniejsza.
Michaël Hoarau
Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes.The book begins with Amazon Forecast, where you’ll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You’ll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you’ll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data.By the end of this AWS book, you’ll have understood how to use the three AWS AI services effectively to perform time series analysis.
Tarek A. Atwan
Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch.Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.
Tarek A. Atwan
To use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.
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 Indexing. Implement iSAX in Python to index time series with confidence
Mihalis Tsoukalos
Time series are everywhere, ranging from financial data and system metrics to weather stations and medical records. Being able to access, search, and compare time series data quickly is essential, and this comprehensive guide enables you to do just that by helping you explore SAX representation and the most effective time series index, iSAX.The book begins by teaching you about the implementation of SAX representation in Python as well as the iSAX index, along with the required theory sourced from academic research papers. The chapters are filled with figures and plots to help you follow the presented topics and understand key concepts easily. But what makes this book really great is that it contains the right amount of knowledge about time series indexing using the right amount of theory and practice so that you can work with time series and develop time series indexes successfully. Additionally, the presented code can be easily ported to any other modern programming language, such as Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript.By the end of this book, you'll have learned how to harness the power of iSAX and SAX representation to efficiently index and analyze time series data and will be equipped to develop your own time series indexes and effectively work with time series data.