Exercise : Self-Taught Learning

一个Self Taught Learning的简单例子

idea: Concretely, for each example in the the labeled training dataset xl, we forward propagate the example through a convolutional and a pooling layer to obtain the activation of the hidden units a(2). We now represent this example using a(2) (the “…

Andrew Ng机器学习四：Neural Networks Learning

背景:跟上一讲一样,识别手写数字,给一组数据集ex4data1.mat,,每个样例都为灰度化为20*20像素,也就是每个样例的维度为400,加载这组数据后,我们会有5000*400的矩阵X(5000个样例),5000*1的矩阵y(表示每个样例所代表的数据).现在让你拟合出一个模型,使得这个模型能很好的预测其它手写的数字. (注意:我们用10代表0(矩阵y也是这样),因为Octave的矩阵没有0行) 一:神经网络( Neural Networks) 神经网络脚本ex4.m: %% Machine…

Unsupervised Feature Learning and Deep Learning(UFLDL) Exercise 总结

7.27 暑假开始后,稍有时间,“搞完”金融项目,便开始跑跑 Deep Learning的程序 Hinton 在Nature上文章的代码跑了3天也没跑完后来Debug 把batch 从200改到20 勉强跑出结果后来开始看文章等感觉晕晕乎乎又翻到:Deep Learning Tutorials 装Theano等,但是python 代码 Debug真是好生恶心再后来翻到 UFLDL,看着有Exercise 便做了起来. 用了5天刷了9个Exercises. 大概年后吧,在微博上看…

【DeepLearning】Exercise:Learning color features with Sparse Autoencoders

Exercise:Learning color features with Sparse Autoencoders 习题链接:Exercise:Learning color features with Sparse Autoencoders sparseAutoencoderLinearCost.m function [cost,grad,features] = sparseAutoencoderLinearCost(theta, visibleSize, hiddenSize, ... lam…

【DeepLearning】Exercise:Self-Taught Learning

Exercise:Self-Taught Learning 习题链接:Exercise:Self-Taught Learning feedForwardAutoencoder.m function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data) % theta: trained weights from the autoencoder % visibleSize: the number of…

Exercise : Self-Taught Learning

First, you will train your sparse autoencoder on an "unlabeled" training dataset of handwritten digits. This produces feature that are penstroke-like. We then extract these learned features from a labeled dataset of handwritten digits. These fea…

MachineLearning Exercise 4 ：Neural Networks Learning

nnCostFunction 消耗公式: a1 = [ones(m,) X]; z2 = a1*Theta1'; pre = sigmoid(a1*Theta1'); a2 = [ones(m,) pre]; z3 = a2*Theta2'; a3 = sigmoid(z3); y_vec = zeros(m,num_labels); :m; y_vec(i,y(i)) = ; end :m J = J + y_vec(i,:)*log(a3(i,:)')+(1-y_vec(i,:))*log(…

How do I learn machine learning?

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644 How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

A Brief Overview of Deep Learning

A Brief Overview of Deep Learning (This is a guest post by Ilya Sutskever on the intuition behind deep learning as well as some very useful practical advice. Many thanks to Ilya for such a heroic effort!) Deep Learning is really popular these days. B…

深度学习Deep learning

In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks. That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow…

【转】The most comprehensive Data Science learning plan for 2017

I joined Analytics Vidhya as an intern last summer. I had no clue what was in store for me. I had been following the blog for some time and liked the community, but did not know what to expect as an intern. The initial few days were good – all the in…

[C5] Andrew Ng - Structuring Machine Learning Projects

About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been…

[C3] Andrew Ng - Neural Networks and Deep Learning

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s…

[C2P2] Andrew Ng - Machine Learning

##Linear Regression with One Variable Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradi…

GAME-BASED LEARNING

https://collegestar.org/modules/game-based-learning Introduction Appalachian State University Game-based learning has been growing in popularity for years, with researchers noticing that good game experiences and effective educational experiences h…

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…

Stanford coursera Andrew Ng 机器学习课程编程作业（Exercise 2）及总结

Exercise 1:Linear Regression---实现一个线性回归关于如何实现一个线性回归,请参考:http://www.cnblogs.com/hapjin/p/6079012.html Exercise 2:Logistic Regression---实现一个逻辑回归问题描述:用逻辑回归根据学生的考试成绩来判断该学生是否可以入学. 这里的训练数据(training instance)是学生的两次考试成绩,以及TA是否能够入学的决定(y=0表示成绩不合格,不予录取:y=1表示录…

Stanford coursera Andrew Ng 机器学习课程编程作业（Exercise 1）

Exercise 1:Linear Regression---实现一个线性回归在本次练习中,需要实现一个单变量的线性回归.假设有一组历史数据<城市人口,开店利润>,现需要预测在哪个城市中开店利润比较好? 历史数据如下:第一列表示城市人口数,单位为万人:第二列表示利润,单位为10,000$ 5.5277 9.1302 8.5186 13.6620 7.0032 11.8540 ..... ...... 用Matlab画出的图形如下:首先加载数据,将data中的第一列数据保存到X中,将data中…

Deep Learning 13_深度学习UFLDL教程：Independent Component Analysis_Exercise（斯坦福大学深度学习教程）

前言理论知识:UFLDL教程.Deep learning:三十三(ICA模型).Deep learning:三十九(ICA模型练习) 实验环境:win7, matlab2015b,16G内存,2T机械硬盘难点:本实验难点在于运行时间比较长,跑一次都快一天了,并且我还要验证各种代价函数的对错,所以跑了很多次. 实验内容:Exercise:Independent Component Analysis.从数据库Sampled 8x8 patches from the STL-10 dataset…

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 11_深度学习UFLDL教程：数据预处理（斯坦福大学深度学习教程）

理论知识:UFLDL数据预处理和http://www.cnblogs.com/tornadomeet/archive/2013/04/20/3033149.html 数据预处理是深度学习中非常重要的一步!如果说原始数据的获得,是深度学习中最重要的一步,那么获得原始数据之后对它的预处理更是重要的一部分. 1.数据预处理的方法: ①数据归一化: 简单缩放:对数据的每一个维度的值进行重新调节,使其在 [0,1]或[ − 1,1] 的区间内逐样本均值消减:在每个样本上减去数据的统计平均值,用于平稳的数…

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…

【Exercise : Self-Taught Learning】的更多相关文章

一个Self Taught Learning的简单例子

Andrew Ng机器学习四：Neural Networks Learning

Unsupervised Feature Learning and Deep Learning(UFLDL) Exercise 总结

【DeepLearning】Exercise:Learning color features with Sparse Autoencoders

【DeepLearning】Exercise:Self-Taught Learning

Exercise : Self-Taught Learning

MachineLearning Exercise 4 ：Neural Networks Learning

How do I learn machine learning?

A Brief Overview of Deep Learning

深度学习Deep learning

【转】The most comprehensive Data Science learning plan for 2017

[C5] Andrew Ng - Structuring Machine Learning Projects

[C3] Andrew Ng - Neural Networks and Deep Learning

[C2P2] Andrew Ng - Machine Learning

GAME-BASED LEARNING

Deep learning：五十一(CNN的反向求导及练习)

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）

Stanford coursera Andrew Ng 机器学习课程编程作业（Exercise 2）及总结

Stanford coursera Andrew Ng 机器学习课程编程作业（Exercise 1）

Deep Learning 13_深度学习UFLDL教程：Independent Component Analysis_Exercise（斯坦福大学深度学习教程）

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

Deep Learning 11_深度学习UFLDL教程：数据预处理（斯坦福大学深度学习教程）

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）

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

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）

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）

Deep Learning 6_深度学习UFLDL教程：Softmax Regression_Exercise（斯坦福大学深度学习教程）

Deep Learning 5_深度学习UFLDL教程：PCA and Whitening_Exercise（斯坦福大学深度学习教程）

Deep Learning 4_深度学习UFLDL教程：PCA in 2D_Exercise（斯坦福大学深度学习教程）

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）