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PyTorch非常容易学习,并提供了一些高级特性,比如支持多处理器,以及分布式和并行计算。PyTorch有一个预训练模型库,为图像分类提供开箱即用的解决方案。PyTorch提供了进入尖端深度学习的最易访问的切入点之一。它与Python编程语言紧密集成,因此对于Python程序员来说,编写它似乎是自然和直观的。独特的、动态的处理计算图的方法意味着PyTorch既高效又灵活。

本书是为那些想要使用PyTorch进行深度学习的人而写的。目的是通过直接实验让您了解深度学习模型。这本书非常适合那些熟悉Python,了解一些机器学习基础知识,并正在寻找一种方法来有效地发展他们的技能的人。这本书将集中在最重要的特征和给出实际的例子。它假设您有Python的工作知识,并熟悉相关的数学思想,包括线性代数和微分。这本书提供了足够的理论,让你开始和运行,不需要严格的数学理解。在本书结束时,您将有一个深度学习系统的实用知识,并能够应用PyTorch模型来解决您关心的问题。

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The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices. KeOps alleviates the major bottleneck of tensor-centric libraries for kernel and geometric applications: memory consumption. It also supports automatic differentiation and outperforms standard GPU baselines, including PyTorch CUDA tensors or the Halide and TVM libraries. KeOps combines optimized C++/CUDA schemes with binders for high-level languages: Python (Numpy and PyTorch), Matlab and GNU R. As a result, high-level "quadratic" codes can now scale up to large data sets with millions of samples processed in seconds. KeOps brings graphics-like performances for kernel methods and is freely available on standard repositories (PyPi, CRAN). To showcase its versatility, we provide tutorials in a wide range of settings online at \url{www.kernel-operations.io}.

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The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices. KeOps alleviates the major bottleneck of tensor-centric libraries for kernel and geometric applications: memory consumption. It also supports automatic differentiation and outperforms standard GPU baselines, including PyTorch CUDA tensors or the Halide and TVM libraries. KeOps combines optimized C++/CUDA schemes with binders for high-level languages: Python (Numpy and PyTorch), Matlab and GNU R. As a result, high-level "quadratic" codes can now scale up to large data sets with millions of samples processed in seconds. KeOps brings graphics-like performances for kernel methods and is freely available on standard repositories (PyPi, CRAN). To showcase its versatility, we provide tutorials in a wide range of settings online at \url{www.kernel-operations.io}.

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