Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
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 book 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.
Deep Learning with R for Beginners by Joshua F. Wiley, Mark Hodnett, Yuxi (Hayden) Liu and Pablo Maldonado
For an optimal student experience, we recommend the following hardware configuration:
You’ll also need the following software installed in advance: