Deep learning with R / Abhijit Ghatak
نوع المادة : نصاللغة: الإنجليزية الناشر:Singapore : Springer, 2019وصف:xxiii, 245 pages : illustrations (some color) ; 24 cmنوع المحتوى:- text
- unmediated
- volume
- 9789811358494
- 9789811358500
- 9811358508
- Q325.5 .G437 2019
نوع المادة | المكتبة الحالية | رقم الطلب | رقم النسخة | حالة | تاريخ الإستحقاق | الباركود | |
---|---|---|---|---|---|---|---|
كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | Q325.5 .G437 2019 (إستعراض الرف(يفتح أدناه)) | C.1 | Library Use Only | داخل المكتبة فقط | 30020000207118 | ||
كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | Q325.5 .G437 2019 (إستعراض الرف(يفتح أدناه)) | C.2 | المتاح | 30020000207117 |
Browsing UAE Federation Library | مكتبة اتحاد الإمارات shelves, Shelving location: General Collection | المجموعات العامة إغلاق مستعرض الرف(يخفي مستعرض الرف)
Q325.5 .E883 2019 Machine Learning with Microsoft technologies : selecting the right architecture and tools for your project / | Q325.5 .F678 2019 Generative deep learning : teaching machines to paint, write, compose, and play / | Q325.5 .G437 2019 Deep learning with R / | Q325.5 .G437 2019 Deep learning with R / | Q325.5 .H477 2016 Multiple instance learning : foundations and algorithms / | Q325.5 .H836 2019 Compact and fast machine learning accelerator for IoT devices / | Q325.5 .H836 2019 Compact and fast machine learning accelerator for IoT devices / |
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