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: $1,600
  • 貴賓價: 9.5$1,520
  • 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 深度學習

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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.