Deep-Learning-with-PyTorch

acknowledgments
We are deeply indebted to the PyTorch team. It is through their collective effort that
PyTorch grew organically from a summer internship project to a world-class deep
learning tool. We would like to mention Soumith Chintala and Adam Paszke, who, in
addition to their technical excellence, worked actively toward adopting a “community
first” approach to managing the project. The level of health and inclusiveness in the
PyTorch community is a testament to their actions.
Speaking of community, PyTorch would not be what it is if not for the relentless
work of individuals helping early adopters and experts alike on the discussion forum.
Of all the honorable contributors, Piotr Bialecki deserves our particular badge of gratitude.
Speaking of the book, a particular shout-out goes to Joe Spisak, who believed in
the value that this book could bring to the community, and also Jeff Smith, who did an
incredible amount of work to bring that value to fruition. Bruce Lin’s work to excerpt
part 1 of this text and provide it to the PyTorch community free of charge is also
hugely appreciated.
We would like to thank the team at Manning for guiding us through this journey,
always aware of the delicate balance between family, job, and writing in our respective
lives. Thanks to Erin Twohey for reaching out and asking if we’d be interested in writing
a book, and thanks to Michael Stephens for tricking us into saying yes. We told you
we had no time! Brian Hanafee went above and beyond a reviewer’s duty. Arthur
Zubarev and Kostas Passadis gave great feedback, and Jennifer Houle had to deal with
our wacky art style. Our copyeditor, Tiffany Taylor, has an impressive eye for detail;
any mistakes are ours and ours alone. We would also like to thank our project editor,
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