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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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Reproducibility is a crucial requirement in scientific research. When results of research studies and scientific papers have been found difficult or impossible to reproduce, we face a challenge which is called reproducibility crisis. Although the demand for reproducibility in Machine Learning (ML) is acknowledged in the literature, a main barrier is inherent non-determinism in ML training and inference. In this paper, we establish the fundamental factors that cause non-determinism in ML systems. A framework, ReproduceML, is then introduced for deterministic evaluation of ML experiments in a real, controlled environment. ReproduceML allows researchers to investigate software configuration effects on ML training and inference. Using ReproduceML, we run a case study: investigation of the impact of bugs inside ML libraries on performance of ML experiments. This study attempts to quantify the impact that the occurrence of bugs in a popular ML framework, PyTorch, has on the performance of trained models. To do so, a comprehensive methodology is proposed to collect buggy versions of ML libraries and run deterministic ML experiments using ReproduceML. Our initial finding is that there is no evidence based on our limited dataset to show that bugs which occurred in PyTorch do affect the performance of trained models. The proposed methodology as well as ReproduceML can be employed for further research on non-determinism and bugs.

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