Deep Learning with R for Beginners
Deep Learning with R for Beginners:
Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
by Mark Hodnett
English | 2019 | ISBN: 1838642706 | 612 Pages | EPUB | 18 MB
Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
by Mark Hodnett
English | 2019 | ISBN: 1838642706 | 612 Pages | EPUB | 18 MB
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.