Book Image

Advanced Deep Learning with R

By : Bharatendra Rai
Book Image

Advanced Deep Learning with R

By: Bharatendra Rai

Overview of this book

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. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Revisiting Deep Learning Basics
3
Section 2: Deep Learning for Prediction and Classification
6
Section 3: Deep Learning for Computer Vision
12
Section 4: Deep Learning for Natural Language Processing
17
Section 5: The Road Ahead

Types of autoencoders

Autoencoder neural networks consist of two main parts:

  • The first part is called the encoder, which reduces the dimensions of the input data. Generally, this is an image. When data from an input image is passed through a network that leads to a lower dimension, the network is forced to extract only the most important features of the input data.
  • The second part of the autoencoder is called the decoder and it tries to reconstruct the original data from whatever is available from the output of the encoder. The autoencoder network is trained by specifying what output this network should try to match.

Let's consider some examples where we will use image data. If the output that's specified is the same image that was given as input, then after training, the autoencoder network is expected to provide an image with a lower resolution that retains the key...