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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Hands-On Deep Learning with R - A practical guide to designing, building, and improving neural network models using R (Paperback)
Loot Price: R1,113
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Hands-On Deep Learning with R - A practical guide to designing, building, and improving neural network models using R (Paperback)
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Explore and implement deep learning to solve various real-world
problems using modern R libraries such as TensorFlow, MXNet, H2O,
and Deepnet Key Features Understand 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 domains Book 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 learn Design 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 R Who 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.
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