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Machine Learning In Pure Mathematics And Theoretical Physics (Hardcover)
Loot Price: R3,849
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Machine Learning In Pure Mathematics And Theoretical Physics (Hardcover)
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The juxtaposition of 'machine learning' and 'pure mathematics and
theoretical physics' may first appear as contradictory in terms.
The rigours of proofs and derivations in the latter seem to reside
in a different world from the randomness of data and statistics in
the former. Yet, an often under-appreciated component of
mathematical discovery, typically not presented in a final draft,
is experimentation: both with ideas and with mathematical data.
Think of the teenage Gauss, who conjectured the Prime Number
Theorem by plotting the prime-counting function, many decades
before complex analysis was formalized to offer a proof.Can modern
technology in part mimic Gauss's intuition? The past five years saw
an explosion of activity in using AI to assist the human mind in
uncovering new mathematics: finding patterns, accelerating
computations, and raising conjectures via the machine learning of
pure, noiseless data. The aim of this book, a first of its kind, is
to collect research and survey articles from experts in this
emerging dialogue between theoretical mathematics and machine
learning. It does not dwell on the well-known multitude of
mathematical techniques in deep learning, but focuses on the
reverse relationship: how machine learning helps with mathematics.
Taking a panoramic approach, the topics range from combinatorics to
number theory, and from geometry to quantum field theory and string
theory. Aimed at PhD students as well as seasoned researchers, each
self-contained chapter offers a glimpse of an exciting future of
this symbiosis.
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