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Deep Learning and Scientific Computing with R torch
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Deep Learning and Scientific Computing with R torch
Current price: $190.00
Barnes and Noble
Deep Learning and Scientific Computing with R torch
Current price: $190.00
Loading Inventory...
Size: Hardcover
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torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++.
Though still "young" as a project, R
torch
already has a vibrant community of users and developers. Experience shows that
users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:
Provide a thorough introduction to
basics – both by carefully explaining underlying concepts and ideas,
and
showing enough examples for the reader to become "fluent" in
Again with a focus on conceptual explanation, show how to use
in deep-learning applications, ranging from image recognition over time series prediction to audio classification
Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by
code you can play with.
Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
Though still "young" as a project, R
torch
already has a vibrant community of users and developers. Experience shows that
users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:
Provide a thorough introduction to
basics – both by carefully explaining underlying concepts and ideas,
and
showing enough examples for the reader to become "fluent" in
Again with a focus on conceptual explanation, show how to use
in deep-learning applications, ranging from image recognition over time series prediction to audio classification
Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by
code you can play with.
Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++.
Though still "young" as a project, R
torch
already has a vibrant community of users and developers. Experience shows that
users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:
Provide a thorough introduction to
basics – both by carefully explaining underlying concepts and ideas,
and
showing enough examples for the reader to become "fluent" in
Again with a focus on conceptual explanation, show how to use
in deep-learning applications, ranging from image recognition over time series prediction to audio classification
Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by
code you can play with.
Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
Though still "young" as a project, R
torch
already has a vibrant community of users and developers. Experience shows that
users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:
Provide a thorough introduction to
basics – both by carefully explaining underlying concepts and ideas,
and
showing enough examples for the reader to become "fluent" in
Again with a focus on conceptual explanation, show how to use
in deep-learning applications, ranging from image recognition over time series prediction to audio classification
Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by
code you can play with.
Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
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