|
Showing 1 - 3 of
3 matches in All Departments
This textbook introduces readers to the theoretical aspects of
machine learning (ML) algorithms, starting from simple neuron
basics, through complex neural networks, including generative
adversarial neural networks and graph convolution networks. Most
importantly, this book helps readers to understand the concepts of
ML algorithms and enables them to develop the skills necessary to
choose an apt ML algorithm for a problem they wish to
solve. In addition, this book includes numerous case studies,
ranging from simple time-series forecasting to object recognition
and recommender systems using massive databases. Lastly, this
book also provides practical implementation examples and
assignments for the readers to practice and improve their
programming capabilities for the ML applications.
The transition towards exascale computing has resulted in major
transformations in computing paradigms. The need to analyze and
respond to such large amounts of data sets has led to the adoption
of machine learning (ML) and deep learning (DL) methods in a wide
range of applications. One of the major challenges is the fetching
of data from computing memory and writing it back without
experiencing a memory-wall bottleneck. To address such concerns,
in-memory computing (IMC) and supporting frameworks have been
introduced. In-memory computing methods have ultra-low power and
high-density embedded storage. Resistive Random-Access Memory
(ReRAM) technology seems the most promising IMC solution due to its
minimized leakage power, reduced power consumption and smaller
hardware footprint, as well as its compatibility with CMOS
technology, which is widely used in industry. In this book, the
authors introduce ReRAM techniques for performing distributed
computing using IMC accelerators, present ReRAM-based IMC
architectures that can perform computations of ML and
data-intensive applications, as well as strategies to map ML
designs onto hardware accelerators. The book serves as a bridge
between researchers in the computing domain (algorithm designers
for ML and DL) and computing hardware designers.
This textbook introduces readers to the theoretical aspects of
machine learning (ML) algorithms, starting from simple neuron
basics, through complex neural networks, including generative
adversarial neural networks and graph convolution networks. Most
importantly, this book helps readers to understand the concepts of
ML algorithms and enables them to develop the skills necessary to
choose an apt ML algorithm for a problem they wish to solve. In
addition, this book includes numerous case studies, ranging from
simple time-series forecasting to object recognition and
recommender systems using massive databases. Lastly, this book also
provides practical implementation examples and assignments for the
readers to practice and improve their programming capabilities for
the ML applications.
|
You may like...
Queen Of Me
Shania Twain
CD
R195
R175
Discovery Miles 1 750
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|