Bharatendra Rai - Advanced Deep Learning with R

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Bharatendra Rai is a chairperson and professor of business analytics, and the director of the Master of Science in Technology Management program at the Charlton College of Business at UMass Dartmouth. He received a Ph.D. in industrial engineering from Wayne State University, Detroit. He received a master's in quality, reliability, and OR from Indian Statistical Institute, India. His current research interests include machine learning and deep learning applications. His deep learning lecture videos on YouTube are watched in over 198 countries. He has over 20 years of consulting and training experience in industries such as software, automotive, electronics, food, chemicals, and so on, in the areas of data science, machine learning, and supply chain management.

Herbert Ssegane is an IT data scientist at Oshkosh Corporation, USA with extensive experience in machine learning, deep learning, statistical analysis, and environmental modeling. He has worked on multiple projects for The Climate Corporation, Monsanto (now Bayer), Argonne National Laboratory, and the U.S. Forest Services. He holds a Ph.D in biological and agricultural engineering from the University of Georgia, Athens USA.

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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 and provide real-life examples.

This book will help you apply deep learning algorithms in R using advanced examples. It covers variants of neural network models such as ANN, CNN, RNN, LSTM, and others using expert techniques. In the course of reading this book, you will make use of popular deep learning libraries such as Keras-R, TensorFlow-R, and others to implement AI models.

This book is for data scientists, machine learning practitioners, deep learning researchers, and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and a working knowledge of the R programming language are required.

, Revisiting Deep Learning Architecture and Techniques , provides an overview of the deep learning techniques that are covered in this book.

, Deep Neural Networks for Multiclass Classification , covers the necessary steps to apply deep learning neural networks to binary and multiclass classification problems. The steps are illustrated using a churn dataset and include data preparation, one-hot encoding, model fitting, model evaluation, and prediction.

, Deep Neural Networks for Regression , illustrates how to develop a prediction model for numeric response. Using the Boston Housing example, this chapter introduces the steps for data preparation, model creation, model fitting, model evaluation, and prediction.

, Image Classification and Recognition , illustrates the use of deep learning neural networks for image classification and recognition using the Keras package with the help of an easy-to-follow example. The steps involved include exploring image data, resizing and reshaping images, one-hot encoding, developing a sequential model, compiling the model, fitting the model, evaluating the model, prediction, and model performance assessment using a confusion matrix.