Deep Learning with R

Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks. Annotation This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning

neural Computing Is One Of The Most Interesting And Rapidly Growing Areas Of Research, Attracting Researchers From A Wide Variety Of Scientific Disciplines. Starting From The Basics, Neural Computing Covers All The Major Approaches, Putting Each In Perspective In Terms Of Their Capabilities, Advantages, And Disadvantages. The Book Also Highlights The Applications Of Each Approach And Explores The Relationships Among Models Developed And Between The Brain And Its Function.

a Comprehensive And Comprehensible Introduction To The Subject, This Book Is Ideal For Undergraduates In Computer Science, Physicists, Communications Engineers, Workers Involved In Artificial Intelligence, Biologists, Psychologists, And Physiologists.

times Higher Education Supplement -

neural Computing Is Easy On The Eye With A Good Layout And Use Of Graphical Icons To Draw Attention To Mathematical Proofs, Algorithms (in Clear Format, Which Would Lend Itself To Computer Implementation) And Summary Sections.

Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function. A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists. An Introduction to Neural Computing has been updated to include new areas of application for neural networks which include neurocontrol and financial forecasting; a description of commercial and industrial projects - data mining, condition monitoring, neuroforecasting, process monitoring and pattern analysis; a revised chapter on the 'weightless' or 'lookup' neural network and a new chapter on the latest research including a discussion of the introduction of intentionality into computing through neural systems and a research programme, 'artificial consciousness.' Since the first edition of this book was published, much has happened in the field of neural networks. The authors reflect these changes by updating and introducing material on new developments including neurocontrol, pattern analysis and dynamic systems. This book should be useful for undergraduate students of neural networks An explanation of the basic concepts of neural computation, this book is about the whole field of neural networks and covers the major approaches and their results. It aims to develop concepts and ideas from their simple basics through their formulation into power computational systems. The second edition of this text has been updated and includes material on new developments including neurocontrol, pattern analysis and dynamic systems. The book should be useful for undergraduate students of neural networks