官术网_书友最值得收藏!

Introduction

In the previous chapter, we looked at a traditional deep feedforward neural network. One of the limitations of a traditional deep feedforward neural network is that it is not translation-invariant, that is, a cat image in the upper-right corner of an image would be considered different from an image that has a cat in the center of the image. Additionally, traditional neural networks are affected by the scale of an object. If the object is big in the majority of the images and a new image has the same object in it but with a smaller scale (occupies a smaller portion of the image), traditional neural networks are likely to fail in classifying the image.

Convolutional Neural Networks (CNNs) are used to deal with such issues. Given that a CNN is able to deal with translation in images and also the scale of images, it is considered a lot more useful in object classification/ detection.

In this chapter, you will learn about the following:

  • Inaccuracy of traditional neural network when images are translated
  • Building a CNN from scratch using Python
  • Using CNNs to improve image classification on a MNIST dataset
  • Implementing data augmentation to improve network accuracy
  • Gender classification using CNNs
主站蜘蛛池模板: 普兰县| 容城县| 昆山市| 孟州市| 怀化市| 鹰潭市| 昌都县| 灵山县| 根河市| 上饶市| 曲沃县| 牡丹江市| 勐海县| 五常市| 勐海县| 离岛区| 虎林市| 阳东县| 巍山| 辽源市| 镇江市| 彝良县| 子长县| 霍城县| 驻马店市| 渭南市| 大悟县| 麦盖提县| 玛纳斯县| 东方市| 孝感市| 鲜城| 昌吉市| 奉新县| 甘德县| 怀远县| 宁蒗| 都江堰市| 山西省| 南昌市| 锡林郭勒盟|