Deep Learning: Activation Function

浅谈深度学习中的激活函数 - The Activation Function in Deep Learning

原文地址:http://www.cnblogs.com/rgvb178/p/6055213.html版权声明:本文为博主原创文章,未经博主允许不得转载. 激活函数的作用首先,激活函数不是真的要去激活什么.在神经网络中,激活函数的作用是能够给神经网络加入一些非线性因素,使得神经网络可以更好地解决较为复杂的问题.比如在下面的这个问题中:如上图(图片来源),在最简单的情况下,数据是线性可分的,只需要一条直线就已经能够对样本进行很好地分类.但如果情况变得复杂了一点呢?在上图中(图片来源),数据就变成了…

The Activation Function in Deep Learning 浅谈深度学习中的激活函数

原文地址:http://www.cnblogs.com/rgvb178/p/6055213.html 版权声明:本文为博主原创文章,未经博主允许不得转载. 激活函数的作用首先,激活函数不是真的要去激活什么.在神经网络中,激活函数的作用是能够给神经网络加入一些非线性因素,使得神经网络可以更好地解决较为复杂的问题. 比如在下面的这个问题中: 如上图(图片来源),在最简单的情况下,数据是线性可分的,只需要一条直线就已经能够对样本进行很好地分类. 但如果情况变得复杂了一点呢?在上图中(图片来源),数据…

Deep learning：五十一(CNN的反向求导及练习)

前言: CNN作为DL中最成功的模型之一,有必要对其更进一步研究它.虽然在前面的博文Stacked CNN简单介绍中有大概介绍过CNN的使用,不过那是有个前提的:CNN中的参数必须已提前学习好.而本文的主要目的是介绍CNN参数在使用bp算法时该怎么训练,毕竟CNN中有卷积层和下采样层,虽然和MLP的bp算法本质上相同,但形式上还是有些区别的,很显然在完成CNN反向传播前了解bp算法是必须的.本文的实验部分是参考斯坦福UFLDL新教程UFLDL:Exercise: Convolutional Ne…

Deep Learning 19_深度学习UFLDL教程：Convolutional Neural Network_Exercise（斯坦福大学深度学习教程）

理论知识:Optimization: Stochastic Gradient Descent和Convolutional Neural Network CNN卷积神经网络推导和实现.Deep learning:五十一(CNN的反向求导及练习) Deep Learning 学习随记(八)CNN(Convolutional neural network)理解 ufldl学习笔记与编程作业:Convolutional Neural Network(卷积神经网络) [UFLDL]Exercise: Co…

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Deep Learning 12_深度学习UFLDL教程：Sparse Coding_exercise（斯坦福大学深度学习教程）

前言理论知识:UFLDL教程.Deep learning:二十六(Sparse coding简单理解).Deep learning:二十七(Sparse coding中关于矩阵的范数求导).Deep learning:二十九(Sparse coding练习) 实验环境:win7, matlab2015b,16G内存,2T机械硬盘本节实验比较不好理解也不好做,我看很多人最后也没得出好的结果,所以得花时间仔细理解才行. 实验内容:Exercise:Sparse Coding.从10张512*51…

Deep Learning 10_深度学习UFLDL教程：Convolution and Pooling_exercise（斯坦福大学深度学习教程）

前言理论知识:UFLDL教程和http://www.cnblogs.com/tornadomeet/archive/2013/04/09/3009830.html 实验环境:win7, matlab2015b,16G内存,2T机械硬盘实验内容:Exercise:Convolution and Pooling.从2000张64*64的RGB图片(它是the STL10 Dataset的一个子集)中提取特征作为训练数据集,训练softmax分类器,然后从3200张64*64的RGB图片(它是th…

Deep Learning 9_深度学习UFLDL教程：linear decoder_exercise（斯坦福大学深度学习教程）

前言实验内容:Exercise:Learning color features with Sparse Autoencoders.即:利用线性解码器,从100000张8*8的RGB图像块中提取颜色特征,这些特征会被用于下一节的练习理论知识:线性解码器和http://www.cnblogs.com/tornadomeet/archive/2013/04/08/3007435.html 实验基础说明: 1.为什么要用线性解码器,而不用前面用过的栈式自编码器等?即:线性解码器的作用? 这一点,Ng…

Deep Learning 8_深度学习UFLDL教程：Stacked Autocoders and Implement deep networks for digit classification_Exercise（斯坦福大学深度学习教程）

前言 1.理论知识:UFLDL教程.Deep learning:十六(deep networks) 2.实验环境:win7, matlab2015b,16G内存,2T硬盘 3.实验内容:Exercise: Implement deep networks for digit classification.利用深度网络完成MNIST手写数字数据库中手写数字的识别.即:用6万个已标注数据(即:6万张28*28的图像块(patches)),作为训练数据集,然后把它输入到栈式自编码器中,它的第一层自编码器…

Deep Learning 7_深度学习UFLDL教程：Self-Taught Learning_Exercise（斯坦福大学深度学习教程）

前言理论知识:自我学习练习环境:win7, matlab2015b,16G内存,2T硬盘练习内容及步骤:Exercise:Self-Taught Learning.具体如下: 一是用29404个无标注数据unlabeledData(手写数字数据库MNIST Dataset中数字为5-9的数据)来训练稀疏自动编码器,得到其权重参数opttheta.这一步的目的是提取这些数据的特征,虽然我们不知道它提取的究竟是哪些特征(当然,可以通过可视化结果看出来,可假设其提取的特征为Features),但…

Deep Learning 1_深度学习UFLDL教程：Sparse Autoencoder练习（斯坦福大学深度学习教程）

1前言本人写技术博客的目的,其实是感觉好多东西,很长一段时间不动就会忘记了,为了加深学习记忆以及方便以后可能忘记后能很快回忆起自己曾经学过的东西. 首先,在网上找了一些资料,看见介绍说UFLDL很不错,很适合从基础开始学习,Adrew Ng大牛写得一点都不装B,感觉非常好,另外对我们英语不好的人来说非常感谢,此教程的那些翻译者们!如余凯等.因为我先看了一些深度学习的文章,但是感觉理解得不够,一般要自己编程或者至少要看懂别人的程序才能理解深刻,所以我根据该教程的练习,一步一步做起,当然我也参考了…

（转）Deep Learning Research Review Week 1: Generative Adversarial Nets

Adit Deshpande CS Undergrad at UCLA ('19) Blog About Resume Deep Learning Research Review Week 1: Generative Adversarial Nets Starting this week, I’ll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summa…

Deep Learning in a Nutshell: Core Concepts

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（转） Deep Learning in a Nutshell: Core Concepts

Deep Learning in a Nutshell: Core Concepts Share: Posted on November 3, 2015by Tim Dettmers 7 CommentsTagged cuDNN, Deep Learning, Deep Neural Networks, Machine Learning,Neural Networks This post is the first in a series I’ll be writing for Paral…

（转）The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)

Adit Deshpande CS Undergrad at UCLA ('19) Blog About The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) Introduction Link to Part 1Link to Part 2 In this post, we’ll go into summarizing a lot of the new and important develo…

The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near

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Decision Boundaries for Deep Learning and other Machine Learning classifiers

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Caffe —— Deep learning in Practice

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Deep Learning in R

Introduction Deep learning is a recent trend in machine learning that models highly non-linear representations of data. In the past years, deep learning has gained a tremendous momentum and prevalence for a variety of applications (Wikipedia 2016a).…

[Deep Learning] 常用的Active functions & Optimizers

深度学习的基本原理是基于人工神经网络,输入信号经过非线性的active function,传入到下一层神经元:再经过下一层神经元的activate,继续往下传递,如此循环往复,直到输出层.正是因为这些active functions的堆砌,深度学习才被赋予了解决非线性问题的能力.当然,仅仅靠active functions还不足于使得深度学习具有"超能力",训练过程中的优化器对于组织神经网络中的各个神经元起到了至关重要的角色.本文简单汇总一些常用的active functions和op…

读《Deep Learning Tutorial》（台湾大学李宏毅深度学习教学ppt）后杂记

原ppt下载:pan.baidu.com/s/1nv54p9R,密码:3mty 需深入实践并理解的重要概念: Deep Learning: SoftMax Fuction(输出层归一化函数,与sigmoid相似的激活函数,用于解决分类问题(分类大于2时:sigmoid解决二分类问题)) 1) 2)每个neuron的softmax输出:,其中: DNN(Deep Neural Networks): MSE(Means Square Error,均方误差) / CE(Cross Entropy,交叉…

通过Visualizing Representations来理解Deep Learning、Neural network、以及输入样本自身的高维空间结构

catalogue . 引言 . Neural Networks Transform Space - 神经网络内部的空间结构 . Understand the data itself by visualizing high-dimensional input dataset - 输入样本内隐含的空间结构 . Example : Word Embeddings in NLP - text word文本词语串内隐含的空间结构 . Example : Paragraph Vectors in NLP…

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week1, Assignment(Gradient Checking)

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Gradient Checking Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking. You are part of a team working to make mobile payments available globally, and…

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week1, Assignment(Regularization)

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Regularization Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem, if the training dataset is not big enough. Sure it do…