This book has the aim of providing the foundations of deep learning with PyTorch and showing them in action in a real-life project. We strive to provide the key concepts underlying deep learning and show how PyTorch puts them in the hands of practitioners. In the book, we try to provide intuition that will support further exploration, and in doing so we selectively delve into details to show what is going on behind the curtain.
Deep Learning with PyTorch doesn’t try to be a reference book; rather, it’s a conceptual companion that will allow you to independently explore more advanced material online. As such, we focus on a subset of the features offered by PyTorch. The most notable absence is recurrent neural networks, but the same is true for other parts of the PyTorch API
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