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Modern
introduction to machine learning with neural networks. Key principles of
the topic are described alongside cutting-edge
applications.
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| This
modern and self-contained book offers a clear and accessible introduction
to the important topic of machine learning with neural networks. In
addition to describing the mathematical principles of the topic, and its
historical evolution, strong connections are drawn with underlying methods
from statistical physics and current applications within science and
engineering. Closely based around a well-established undergraduate course,
this pedagogical text provides a solid understanding of the key aspects of
modern machine learning with artificial neural networks, for students in
physics, mathematics, and engineering. Numerous exercises expand and
reinforce key concepts within the book and allow students to hone their
programming skills. Frequent references to current research develop a
detailed perspective on the state-of-the-art in machine learning
research. |
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- Accessible and self-contained approach to the
theoretical foundations of machine learning, and its modern applications in
science and engineering.
- Suitable for teaching advanced undergraduate
or graduate courses on this topic for science, mathematics and engineering
students.
- Numerous end of chapter exercises expand and
reinforce key concepts.
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