Recurrent Neural Networks: From Simple to Gated Architectures (Hardback or Cased
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Recurrent Neural Networks: From Simple to Gated Architectures (Hardback or Cased)
In the world of deep learning, recurrent neural networks (RNNs) have become increasingly popular for tasks involving sequential data. From language modeling to speech recognition, RNNs have shown great promise in capturing the temporal dependencies inherent in sequential data.
This book, “Recurrent Neural Networks: From Simple to Gated Architectures,” delves into the various iterations of RNN architectures, starting from the basic vanilla RNN to the more advanced gated architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).
Through a combination of theoretical explanations and practical examples, this book provides a comprehensive overview of RNNs and their applications. Readers will learn how to design and implement RNNs for various tasks, and gain insights into the inner workings of these powerful neural networks.
Whether you are a seasoned deep learning practitioner looking to expand your knowledge or a newcomer interested in delving into the world of RNNs, this book is a valuable resource that will guide you through the complexities of recurrent neural networks. Available in hardback or cased format, this book is a must-have for anyone interested in mastering the intricacies of RNN architectures.
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