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Machine and Deep Learning Algorithms and Applications (Paperback)
Loot Price: R1,549
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Machine and Deep Learning Algorithms and Applications (Paperback)
Series: Synthesis Lectures on Signal Processing
Expected to ship within 10 - 15 working days
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This book introduces basic machine learning concepts and
applications for a broad audience that includes students, faculty,
and industry practitioners. We begin by describing how machine
learning provides capabilities to computers and embedded systems to
learn from data. A typical machine learning algorithm involves
training, and generally the performance of a machine learning model
improves with more training data. Deep learning is a sub-area of
machine learning that involves extensive use of layers of
artificial neural networks typically trained on massive amounts of
data. Machine and deep learning methods are often used in
contemporary data science tasks to address the growing data sets
and detect, cluster, and classify data patterns. Although machine
learning commercial interest has grown relatively recently, the
roots of machine learning go back to decades ago. We note that
nearly all organizations, including industry, government, defense,
and health, are using machine learning to address a variety of
needs and applications. The machine learning paradigms presented
can be broadly divided into the following three categories:
supervised learning, unsupervised learning, and semi-supervised
learning. Supervised learning algorithms focus on learning a
mapping function, and they are trained with supervision on labeled
data. Supervised learning is further sub-divided into
classification and regression algorithms. Unsupervised learning
typically does not have access to ground truth, and often the goal
is to learn or uncover the hidden pattern in the data. Through
semi-supervised learning, one can effectively utilize a large
volume of unlabeled data and a limited amount of labeled data to
improve machine learning model performances. Deep learning and
neural networks are also covered in this book. Deep neural networks
have attracted a lot of interest during the last ten years due to
the availability of graphics processing units (GPU) computational
power, big data, and new software platforms. They have strong
capabilities in terms of learning complex mapping functions for
different types of data. We organize the book as follows. The book
starts by introducing concepts in supervised, unsupervised, and
semi-supervised learning. Several algorithms and their inner
workings are presented within these three categories. We then
continue with a brief introduction to artificial neural network
algorithms and their properties. In addition, we cover an array of
applications and provide extensive bibliography. The book ends with
a summary of the key machine learning concepts.
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