This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.

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使用TensorFlow进行深度学习：评论

这篇综述涵盖了TensorFlow的核心概念和设计决策。TensorFlow最初由Google的研究人员创建，是众多深度学习库中最受欢迎的一种。在深度学习领域，神经网络取得了巨大的成功，并在各个领域获得了广泛的普及。考虑到其灵活性和可扩展性，该系列模型还具有极大的潜力来促进针对教育和行为科学中各种问题的数据分析和建模。我们向读者概述了神经网络模型的基础知识，例如多层感知器，卷积神经网络和随机梯度下降，这是神经网络模型最常用的优化方法。然而，这些模型和优化算法的实现既费时又容易出错。幸运的是，TensorFlow大大简化并加速了神经网络模型的研究和应用。我们回顾了TensorFlow的几个核心概念，例如图形构造函数，图形执行工具以及TensorFlow的可视化工具TensorBoard。然后，我们将这些概念应用于构建和训练卷积神经网络模型来对手写数字进行分类。通过对低层和高层应用程序编程接口的比较以及对图形处理单元支持，分布式培训和使用TensorFlow Probability库的概率建模的讨论来结束本综述。我们回顾了TensorFlow的几个核心概念，例如图形构造函数，图形执行工具以及TensorFlow的可视化工具TensorBoard。然后，我们将这些概念应用于构建和训练卷积神经网络模型来对手写数字进行分类。通过对低层和高层应用程序编程接口的比较以及对图形处理单元支持，分布式培训和使用TensorFlow Probability库的概率建模的讨论来结束本综述。我们回顾了TensorFlow的几个核心概念，例如图形构造函数，图形执行工具以及TensorFlow的可视化工具TensorBoard。然后，我们将这些概念应用于构建和训练卷积神经网络模型来对手写数字进行分类。通过对低层和高层应用程序编程接口的比较以及对图形处理单元支持，分布式培训和使用TensorFlow Probability库的概率建模的讨论来结束本综述。