本文为美国普渡大学（作者：Derek L. Stinson）的硕士论文，共66页。

目前深度学习的研究主要集中在使用Python作为支持语言上。Go是一种新兴的语言，它有许多优点，包括对并发的本地支持，在过去几年中，它的采用率有所上升。然而，由于缺乏模型开发的支持库和框架，这种语言并没有被广泛用于开发学习模型。在本论文中，利用Go进行神经网络模型的一般开发和卷积神经网络的研究。这项研究是基于一个称为GoCuNets神经网络模型的Go-CUDA实现，然后将这个实现与GO-CPU深度学习实现进行比较，后者利用了Go内置的ConvNetGo并发性。对这两种实现方式的比较表明，与ConvNetGo相比，使用GoCuNets时的性能显著提高。

Current research in deep learning is primarily focused on using Python as a support language. Go, an emerging language, that has many benefits including native support for concurrency has seen a rise in adoption over the past few years. However, this language is not widely used to develop learning models due to the lack of supporting libraries and frameworks for model development. In this thesis, the use of Go for the development of neural network models in general and convolution neural networks is explored. The proposed study is based on a Go-CUDA implementation of neural network models called GoCuNets. This implementation is then compared to a Go-CPU deep learning implementation that takes advantage of Go’s built in concurrency called ConvNetGo. A comparison of these two implementations shows a significant performance gain when using GoCuNets compared to ConvNetGo.

1.       引言

2. 相关工作

3. 研究方法

4. 结果

5. 结论

附录A ConvNetGo

附录B GoCuNets

附录C GoCudnn

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