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1. Revisiting Deep Learning architecture and techniques
2. Deep Neural Networks for multiclass classification
3. Deep Neural Networks for regression
4. Image classification and recognition
5. Image classification using convolutional neural networks
6. Applying Autoencoder neural networks using Keras
7. Image classification for small data using transfer learning
8. Creating new images using generative adversarial networks
9. Deep network for text classification
10. Text classification using recurrent neural networks
11. Text classification using Long Short-Term Memory Network
12. Text classification using convolutional recurrent networks
13. Tips, tricks and the road ahead

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them.

This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network.