Deep Learning with R for Beginners

Hodnett, Mark, Wiley, Joshua F., Liu, Yuxi (Hayden)

  • 出版商: Packt Publishing
  • 出版日期: 2019-05-17
  • 售價: $1,650
  • 貴賓價: 9.5$1,568
  • 語言: 英文
  • 頁數: 612
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838642706
  • ISBN-13: 9781838642709
  • 相關分類: R 語言DeepLearning
  • 立即出貨 (庫存=1)

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商品描述

Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.

 

This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.

 

By the end of this Learning Path, you’ll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.

商品描述(中文翻譯)

深度學習在多個領域中找到了實際應用,而R語言是設計和部署深度學習模型的首選語言。

這個學習路徑將介紹深度學習的基礎知識,甚至教你從頭開始建立神經網絡模型。隨著你逐步閱讀各章節,你將探索深度學習庫,並了解如何為各種挑戰創建深度學習模型,從異常檢測到推薦系統。學習路徑還將幫助你涵蓋高級主題,如生成對抗網絡(GANs)、遷移學習和雲端中的大規模深度學習,以及模型優化、過擬合和數據擴增。通過實際項目,你還將快速掌握在R中訓練卷積神經網絡(CNNs)、循環神經網絡(RNNs)和長短期記憶網絡(LSTMs)的技能。

通過這個學習路徑的結束,你將熟悉深度學習,並具備在研究工作或項目中實施多個深度學習概念所需的技能。