Using Recurrent Neural Networks Time by Continental Academy Press (2 results)

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Recurrent neural networks (RNNs) are a type of deep learning algorithm that's revolutionizing the field of time series prediction. Using Recurrent Neural Networks for Time Series Prediction provides a comprehensive guide to understanding the principles and applications of RNNs, from forecasting stock prices to predicting weather patterns. By mastering the art of RNNs, you'll learn how to develop sophisticated time series prediction systems that can analyze and understand complex data with unprecedented accuracy. This book will walk you through the process of designing, training, and deploying RNNs for a wide range of applications, from finance to healthcare. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Time series forecasting is a critical component of many industries, from finance to healthcare, yet the complexity of these data sets can be overwhelming. 'Using Recurrent Neural Networks for Time Series Forecasting' offers a cutting-edge exploration of recurrent neural networks (RNNs), a powerful technique for modeling and predicting time series data. By examining the applications and limitations of RNNs in time series forecasting, this book provides a comprehensive overview of the latest developments in the field, including the use of long short-term memory (LSTM) networks and gated recurrent units (GRUs). With its focus on practical implementation and real-world examples, readers will gain a deeper understanding of how to harness the power of RNNs to build more accurate, efficient, and effective time series forecasting systems. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.