{"product_id":"9781805128182","title":"Time Series with PyTorch : Modern Deep Learning Toolkit for Real-World Forecasting Challenges","description":"\u003cp\u003eLeverage time series analysis for better decision making with cutting-edge tools and techniques\u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eGet to grips with concepts through jargon-busting explanations\n\u003cbr\u003eLearn to use a variety of datasets that reflect problems you're likely to encounter in everyday practice\n\u003cbr\u003eUnderstand how to select the appropriate algorithms to avoid unnecessary complexity\n\u003cbr\u003eLearn from progressive and pedagogical chapters that guides you from introductory toy problems to end-to-end real-world projects\u003c\/p\u003e\n\n\u003cp\u003eBook DescriptionDeep learning (DL) is a cutting-edge approach to learning from data. While it has taken the areas of computer vision and natural language processing by storm, its application to time-series forecasting is a more recent phenomenon and remains challenging for both new and experienced practitioners.\n\u003cbr\u003eTo develop the best time series models for a real-world problem, it is essential to have not only a thorough understanding of the time series data but also a solid grasp of DL models themselves. This book investigates time series structures and the DL approaches that can address the variety of challenges they present to practitioners in industry.\n\u003cbr\u003eIn this book, you will gain insights from a variety of perspectives, both from the data and the models. You will learn about the complexities of real-world time series data, explore the different problem settings for time series analysis, touch upon the foundation of DL models for time series, and practice end-to-end time series analysis projects when DL works; the authors believe in choosing the best tool for the problem, so traditional methods are never far from our minds. A GitHub repository with coding examples will be provided to support your journey.\n\u003cbr\u003eBy the end of this book, you will be able to approach almost any time series challenge with an appropriate model that gets you results.What you will learn\u003c\/p\u003e\n\n\u003cp\u003eDevelop an understanding of how to code and test neural networks with PyTorch and PyTorch Lightning\n\u003cbr\u003eAddress challenges presented by different data structures with neural architecture\n\u003cbr\u003eLearn advanced methods to evaluate and validate models by comparing and optimizing them and partitioning your data correctly\n\u003cbr\u003eGain insight into how time series models work behind the scenes and why a model fits a particular type of problem\n\u003cbr\u003eApply contemporary approaches like TFT, NBEATs, and NHiTS for individual forecasts and hierarchical modeling\u003c\/p\u003e\n\n\u003cp\u003eWho this book is forThis book is for data analysts, scientists, and students who want to know how to apply deep learning methods to time-series forecasting problems with PyTorch for real-world business problems.\n\u003cbr\u003eWhile the book assumes some understanding of statistics and modeling, you won't need in-depth knowledge of time-series to follow along. Some awareness of Python programming is important, but we do not assume any prior knowledge of PyTorch.\n\u003cbr\u003eThe main goal of this book is to be accessible for those with little or no experience with deep learning methods in time series.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":48872045248747,"sku":"EB_CP0800_F05-03_SIMS","price":95.2,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9781805128182-1.jpg?v=1785900843","url":"https:\/\/kinokuniya.com.sg\/ja\/products\/9781805128182","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}