Deep learning enables efficient and accurate learning from data. Developers working with R will be able to put their knowledge to work with this practical guide to deep learning. The book provides a ...hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time.
Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and DeepnetKey FeaturesUnderstand 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 domainsBook DescriptionDeep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming.
This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You'll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you'll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems.
By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.What you will learnDesign a feedforward neural network to see how the activation function computes an output
Create an image recognition model using convolutional neural networks (CNNs)
Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm
Apply text cleaning techniques to remove uninformative text using NLP
Build, train, and evaluate a GAN model for face generation
Understand the concept and implementation of reinforcement learning in RWho this book is forThis book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.
Technology change is a key driver that motivates the need for organizational learning as a strategic source of human capital development (knowledge, skills, and expertise) and competitive advantage ...for organizational productivity (Beattie, 2006), sustainability (Jones, 2001), and survival (Chatzimouratidis et al., 2012). Despite billions of dollars of annual investment in employee training to improve workforce development, performance, and productivity (WEF, 2019; Paradise, 2007), recent organizational development research has drawn critical attention to workforce e- learning benefits and adoption barriers among professional staff (Becker et al., 2013; Berge & Giles, 2008). Given the growing pressures of global competition and rapid technological change on organizational learning and productivity (Burner et al., 2019; Chatzimouratidis et al., 2012; Marshall, 2011; Beattie, 2006), the purpose of the study was to use Social Cognitive Theory (SCT) (Bandura, 2018) to identify for higher education leaders the factors that most significantly contributed to workplace e-learning activity related to employee professional development and cost-effective, scalable organizational learning. Drawing on organizational development, economics, psychology, and technology literature, this mixed-methods study utilized quantitative (structural equation modeling) and qualitative (research interviews) to reveal the hypothetical extent to which SCT’s triadic interplay of reciprocal determinants (personal, behavioral, environmental) influenced and predicted workplace e-learning activity and continuance intentions among professional staff in a higher education work setting.
