| 書目名稱 | Deep Learning with R | | 編輯 | Abhijit Ghatak | | 視頻video | http://file.papertrans.cn/265/264636/264636.mp4 | | 概述 | Offers a hands on approach to deep learning while explaining the theory and mathematical concepts in an intuitive manner.Broadens the understanding of advanced neural networks including ConvNets and S | | 圖書封面 |  | | 描述 | .?Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning.??.The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks.?. | | 出版日期 | Textbook 2019 | | 關(guān)鍵詞 | Artificial Intelligence; Statistics; Deep neural networks; Regularization and hyper-parameter tuning; Co | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-13-5850-0 | | isbn_ebook | 978-981-13-5850-0 | | copyright | Springer Nature Singapore Pte Ltd. 2019 |
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