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深度学习:核心原理与案例分析
book

深度学习:核心原理与案例分析

by Posts & Telecom Press, Ahmed Menshawy
May 2024
Intermediate to advanced
389 pages
6h 49m
Chinese
Packt Publishing
Content preview from 深度学习:核心原理与案例分析

第5章 TensorFlow基础示例实战

本章将解释TensorFlow背后的主要计算概念,即计算图模型,并演示如何通过实现线性回归和逻辑回归使读者走上学习深度学习知识的正轨。

本章将介绍以下内容。

  • 神经元的结构。
  • 激活函数。
  • 前馈神经网络。
  • 需要多层网络的原因。
  • TensorFlow术语回顾。
  • 构建与训练线性回归模型。
  • 构建与训练逻辑回归模型。

本章将首先解释单个神经元实际上可以做什么,并基于此,引出工程对多层神经网络的需求。接下来,本章将更详细地介绍TensorFlow中使用/可用的主要概念和工具,以及如何使用这些工具构建简单的示例,如线性回归模型和逻辑回归模型。

神经网络是一种计算模型,它主要受到人类大脑的神经网络处理传入信息的方式的启发。神经网络在机器学习研究(特别是深度学习)和行业应用方面取得了巨大突破,例如在计算机视觉、语音识别和文本处理方面取得的突破性成果。在本章中,我们将尝试研究一种特定类型的神经网络,它称为多层感知机

人类大脑的基本计算单元称为神经元,神经系统中有大约860亿个神经元,它们通过1014~1015个突触相连。

图5.1所示为一种生物神经元。图5.2所示为相应的数学模型。在生物神经元中,每个神经元接收来自其树突的输入信号,然后沿其轴突产生输出信号,其中轴突最后被分离并通过突触连接到其他神经元。

..\19-0460 图\5-1.tif

图5.1 大脑的计算单元(图片源自GitHub网站)

图5.2 大脑计算单元的数学模型(图片源自GitHub网站) ...

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Publisher Resources

ISBN: 9781836201212