We expose the statistical foundations of deep learning with the goal of facilitating conversation between the deep learning and statistics communities. We highlight core themes at the intersection; summarize key neural models, such as feedforward neural networks, sequential neural networks, and neural latent variable models; and link these ideas to their roots in probability and statistics. We also highlight research directions in deep learning where there are opportunities for statistical contributions.

中文翻译：

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从统计角度看深度学习简介


我们揭示了深度学习的统计基础，旨在促进深度学习和统计社区之间的对话。我们在交叉点突出核心主题;总结关键神经模型，例如前馈神经网络、序列神经网络和神经潜在变量模型;并将这些想法与它们在概率和统计中的根源联系起来。我们还强调了深度学习中有机会做出统计贡献的研究方向。