Deep Learning with PyTorch, Second Edition, MEAP, Version 3, Antiqa L., Stevens E., Huang H., Viehmann T.

Deep Learning with PyTorch, Second Edition, MEAP, Version 3, Antiqa L., Stevens E., Huang H., Viehmann T.
     
   Thank you for purchasing the MEAP for Deep Learning with PyTorch, Second Edition.
The book is written for developers, students, or even hobbyists who have some prior experience with the Python programming language and want to gain a better understanding of deep learning. There is no better time to learn about deep learning than now as artificial intelligence gains an ever-growing significance in shaping our products and the world around us.




Why PyTorch?
As we’ve said, deep learning allows us to carry out a very wide range of complicated tasks, like machine translation, playing strategy games, or identifying objects in cluttered scenes, by exposing our model to illustrative examples. In order to do so in practice, we need tools that are flexible, so they can be adapted to such a wide range of problems, and efficient, to allow training to occur over large amounts of data in reasonable times; and we need the trained model to perform correctly in the presence of variability in the inputs. Let’s take a look at some of the reasons we decided to use PyTorch.

PyTorch is easy to recommend because of its simplicity. Many researchers and practitioners find it easy to learn, use, extend, and debug. It’s Pythonic, and while like any complicated domain it has caveats and best practices, using the library generally feels familiar to developers who have used Python previously.

More concretely, programming the deep learning machine is very natural in PyTorch. PyTorch gives us a data type, the Tensor, to hold numbers, vectors, matrices, or arrays in general. In addition, it provides functions for operating on them. We can program with them incrementally and, if we want, interactively, just like we are used to from Python. If you know NumPy, this will be very familiar.

CONTENTS.
Welcome.
1 Introducing deep learning and the PyTorch Library.
2 Pretrained networks.
3 It starts with a tensor.
4 Real-world data representation using tensors.
5 The mechanics of learning.
6 Using a neural network to fit the data.
7 Telling birds from airplanes: Learning from images.



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Теги: учебник по программированию :: программирование :: Antiqa :: Stevens :: Huang :: Viehmann