Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R

Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet

• Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem
• Improve models using parameter tuning, feature engineering, and ensembling
• Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domains