April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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自动特征生成：图像特征提取和深度学习
｜
127
图 8-17：训练后 AlexNet 中第二层卷积核的可视化
尽管这个领域取得了重大进展，但图像特征化还是更像一门艺术，而不是科学。十年前，
研究者们使用图像梯度、边缘检测、方向、空间线索、平滑和归一化等技术组合进行手
工特征提取。现在，深度学习体系建立的模型封装了大致相同的技术，但参数是从训练
图像中自动学习的。深度学习的神奇魔力就隐藏在模型更深处的一种抽象之中，等待我
们去探索！
8.4
小结
在接近尾声的时候，我们可以凭借从本章获得的直觉更好地理解：为什么多数简单直观的
图像特征对于执行像图像分类这样的任务并不总是最好的。更重要的事情不是将每个像素
表示成原子单位，而是考虑像素与其邻近像素之间的关系。
SIFT
和
HOG
本来是为其他任
务而开发的，但我们可以修改一下这些技术，通过分析邻域中的梯度更好地从整个图像中
提取特征。
近年来的一次技术飞跃是在计算机视觉领域使用深度神经网络，目的是使图像的特征提取
得到更好的发展。我们需要记住的重点是，深度学习将很多神经网络层和转换层彼此堆叠
在一起。在分别检查时，其中有些层可以提取出相似的特征，这些特征可以认为是人类视
觉的基础组成部分：定义线条、梯度、颜色图。
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.