{"product_id":"deep-learning-for-time-series-cookbook-use-pytorch-and-python-recipes-for-forecasting-classification-and-anomaly-detection-paperback","title":"Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection - Paperback","description":"\u003cp\u003eby \u003cb\u003eVitor Cerqueira\u003c\/b\u003e (Author), \u003cb\u003eLuís Roque\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eLearn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eLearn the fundamentals of time series analysis and how to model time series data using deep learning\u003c\/li\u003e\n\u003cli\u003eExplore the world of deep learning with PyTorch and build advanced deep neural networks\u003c\/li\u003e\n\u003cli\u003eGain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection\u003c\/li\u003e\n\u003cli\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eMost 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eBy the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eGrasp the core of time series analysis and unleash its power using Python\u003c\/li\u003e\n\u003cli\u003eUnderstand PyTorch and how to use it to build deep learning models\u003c\/li\u003e\n\u003cli\u003eDiscover how to transform a time series for training transformers\u003c\/li\u003e\n\u003cli\u003eUnderstand how to deal with various time series characteristics\u003c\/li\u003e\n\u003cli\u003eTackle forecasting problems, involving univariate or multivariate data\u003c\/li\u003e\n\u003cli\u003eMaster time series classification with residual and convolutional neural networks\u003c\/li\u003e\n\u003cli\u003eGet up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs)\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you're a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eGetting Started with Time Series\u003c\/li\u003e\n\u003cli\u003eGetting Started with PyTorch\u003c\/li\u003e\n\u003cli\u003eUnivariate Time Series Forecasting\u003c\/li\u003e\n\u003cli\u003eForecasting with PyTorch Lightning\u003c\/li\u003e\n\u003cli\u003eGlobal Forecasting Models\u003c\/li\u003e\n\u003cli\u003eAdvanced Deep Learning Architectures for Time Series Forecasting\u003c\/li\u003e\n\u003cli\u003eProbabilistic Time Series Forecasting\u003c\/li\u003e\n\u003cli\u003eDeep Learning for Time Series Classification\u003c\/li\u003e\n\u003cli\u003eDeep Learning for Time Series Anomaly Detection\u003c\/li\u003e\n\u003c\/ol\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 274\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.58 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 29, 2024\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":51749988925728,"sku":"9781805129233","price":74.86,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0974\/9764\/5344\/files\/ed0345cbdf2267a2e17c7c71497f875e.webp?v=1779941125","url":"https:\/\/ebocreations.com\/products\/deep-learning-for-time-series-cookbook-use-pytorch-and-python-recipes-for-forecasting-classification-and-anomaly-detection-paperback","provider":"The E-Book Oasis LLC","version":"1.0","type":"link"}