Deep Learning with R for Beginners
- Paperback: 612 pages
- Publisher: WOW! eBook (May 20, 2019)
- Language: English
- ISBN-10: 1838642706
- ISBN-13: 978-1838642709
Deep Learning with R for Beginners: Explore the world of neural networks by building powerful deep learning models using the R ecosystem
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.
- Implement credit card fraud detection with autoencoders
- Train neural networks to perform handwritten digit recognition using MXNet
- Reconstruct images using variational autoencoders
- Explore the applications of autoencoder neural networks in clustering and dimensionality reduction
- Create natural language processing (NLP) models using Keras and TensorFlow in R
- Prevent models from overfitting the data to improve generalizability
- Build shallow neural network prediction models
By the end of this Learning Path Deep Learning with R for Beginners, 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.