Deep Learning with R for Beginners

Deep Learning with R for Beginners

作者: Hodnett Mark Wiley Joshua F. Liu Yuxi (Hayden)
出版社: Packt Publishing
出版在: 2019-05-17
ISBN-13: 9781838642709
ISBN-10: 1838642706
裝訂格式: Quality Paper - also called trade paper
總頁數: 612 頁





內容描述


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.




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