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Deep Learning with R for Beginners - Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet (Paperback)
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Deep Learning with R for Beginners - Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet (Paperback)
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Explore the world of neural networks by building powerful deep
learning models using the R ecosystem Key Features Get to grips
with the fundamentals of deep learning and neural networks Use R
3.5 and its libraries and APIs to build deep learning models for
computer vision and text processing Implement effective deep
learning systems in R with the help of end-to-end projects Book
DescriptionDeep learning finds practical applications in several
domains, while R is the preferred language for designing and
deploying deep learning models. This Learning Path introduces you
to the basics of deep learning and even teaches you to build a
neural network model from scratch. As you make your way through the
chapters, you'll explore deep learning libraries and understand how
to create deep learning models for a variety of challenges, right
from anomaly detection to recommendation systems. The book will
then help you cover advanced topics, such as generative adversarial
networks (GANs), transfer learning, and large-scale deep learning
in the cloud, in addition to model optimization, overfitting, and
data augmentation. Through real-world projects, you'll also get up
to speed with training convolutional neural networks (CNNs),
recurrent neural networks (RNNs), and long short-term memory
networks (LSTMs) in R. By the end of this Learning Path, you'll be
well versed with deep learning and have the skills you need to
implement a number of deep learning concepts in your research work
or projects. This Learning Path includes content from the following
Packt products: R Deep Learning Essentials - Second Edition by
Joshua F. Wiley and Mark Hodnett R Deep Learning Projects by Yuxi
(Hayden) Liu and Pablo Maldonado What you will learn Implement
credit card fraud detection with autoencoders Train neural networks
to perform handwritten digit recognition using MXNet Reconstruct
images using variational autoencoders Explore the applications of
autoencoder neural networks in clustering and dimensionality
reduction Create natural language processing (NLP) models using
Keras and TensorFlow in R Prevent models from overfitting the data
to improve generalizability Build shallow neural network prediction
models Who this book is forThis Learning Path is for aspiring data
scientists, data analysts, machine learning developers, and deep
learning enthusiasts who are well versed in machine learning
concepts and are looking to explore the deep learning paradigm
using R. A fundamental understanding of R programming and
familiarity with the basic concepts of deep learning are necessary
to get the most out of this Learning Path.
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