Notes on Convolutional Neural Networks

这是个06年的老文章了,但是很多地方还是值得看一看的. 一.概要 主要讲了CNN的Feedforward Pass和 Backpropagation Pass,关键是卷积层和polling层的BP推导讲解. 二.经典BP算法 前向传播需要注意的是数据归一化,对训练数据进行归一化到 0 均值和单位方差,可以在梯度下降上改善,因为这样可以防止过早的饱,这主要还是因为早期的sigmoid和tanh作为激活函数的弊端(函数在过大或者过小的时候,梯度都很小),等现在有了RELU和batch normali

Use bigger datasets for CNN in hope of better performance. A new data set for sports video classification: sports-1M. CNN in one frame is about the same as many frames. CNN is good at image but not modeling temporal sequences. The result is not good.

Convolutional Neural Networks Convolutional Neural Networks are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. They still have a loss

Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Layers to Convolutional Layers ConvNet Architectures Layer Patterns Layer Sizing Patterns C

Linear Classification 在上一讲里,我们介绍了图像分类问题以及一个简单的分类模型K-NN模型,我们已经知道K-NN的模型有几个严重的缺陷,第一就是要保存训练集里的所有样本,这个比较消耗存储空间:第二就是要遍历所有的训练样本,这种逐一比较的方式比较耗时而低效. 现在,我们要介绍一种更加强大的图像分类模型,这个模型会很自然地引申出神经网络和Convolutional Neural Networks(CNN),这个模型有两个重要的组成部分,一个是score function,将原始

Convolutional Neural Networks (CNNs / ConvNets) 前面做了如此漫长的铺垫,如今终于来到了课程的重点. Convolutional Neural Networks. 简称CNN,与之前介绍的一般的神经网络相似,CNN相同是由能够学习的权值与偏移量构成.每个神经元接收一些输入.做点积运算加上偏移量,然后选择性的通过一些非线性函数.整个网络终于还是表示成一个可导的loss function,网络的起始端是输入图像.网络的终端是每一类的预測值,通过一个ful

Ahmet Taspinar Home About Contact Building Convolutional Neural Networks with Tensorflow Posted on augustus 15, 2017 adminPosted in convolutional neural networks, deep learning, tensorflow 1. Introduction In the past I have mostly written about 'clas

Setting up the data and the model 前面我们介绍了一个神经元的模型,通过一个激励函数将高维的输入域权值的点积转化为一个单一的输出,而神经网络就是将神经元排列到每一层,形成一个网络结构,这种结构与我们之前介绍的线性模型不太一样,因此score function也需要重新定义,神经网络实现了一系列的线性映射与非线性映射,这一讲,我们主要介绍神经网络的数据预处理以及score function的定义. data processing 给定一个训练集,S={xi∈RD|i

Learning to Compare Image Patches via Convolutional Neural Networks ---  Reading Summary 2017.03.08 Target: this paper attempt to learn a geneal similarity function for comparing image patches from image data directly. There are several ways in which