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This book explores new methods, architectures, tools, and
algorithms for Artificial Intelligence Hardware Accelerators. The
authors have structured the material to simplify readers’ journey
toward understanding the aspects of designing hardware
accelerators, complex AI algorithms, and their computational
requirements, along with the multifaceted applications. Coverage
focuses broadly on the hardware aspects of training, inference,
mobile devices, and autonomous vehicles (AVs) based AI accelerators
The world is approaching a point where big data will start to play
a beneficial role in many industries and organizations. Today,
analyzing data for new insights has become an everyday norm,
increasing the need for data analysts to use efficient and
appropriate tools to provide quick and valuable results to clients.
Existing research in the field currently lacks a full coverage of
all essential algorithms, leaving a knowledge void for practical
implementation and code in Python with all needed libraries and
links to datasets used. Advanced Interdisciplinary Applications of
Machine Learning Python Libraries for Data Science serves as a
one-stop book to help emerging data scientists gain hands-on skills
needed through real-world data and completely up-to-date Python
code. It covers all the technical details, from installing the
needed software to importing libraries and using the latest data
sets; deciding on the right model; training, testing, and
evaluating the model; and including NumPy, Pandas, and matplotlib.
With coverage on various machine learning algorithms like
regression, linear and logical regression, classification, support
vector machine (SVM), clustering, k-nearest neighbor, market basket
analysis, Apriori, k-means clustering, and visualization using
Seaborne, it is designed for academic researchers, undergraduate
students, postgraduate students, executive education program
leaders, and practitioners.
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