http://m.blog.csdn.net/blog/wu010555688/24487301This article has compiled a number of online Daniel's blog, detailed explanation of CNN's basic structure and core ideas, welcome to exchange.[1] Deep Learning Introduction[2] Deep Learning training Process[3] Deep
Python implementation of multilayer neural networks.
The code is pasted first, the programming thing is not explained.
Basic theory reference Next: Deep Learning Learning Notes (iii): Derivation of neural network reverse propagation algorithm
Supervisedlearningmodel, Nnlayer, and softmaxregression that appear in your code, refer to the previous note:
implementation in Toolbox is very simple:In the NNTRAIN.M:batch_x = batch_x.* (rand (Size (batch_x)) >nn.inputzeromaskedfraction)That is, the size of the (nn.inputzeromaskedfraction) part of the X-0,denoising Autoencoder appears to be stronger than sparse autoencoderContractive auto-encoders:This variant is "Contractive auto-encoders:explicit invariance during feature extraction" proposedThis paper also summarizes a bit of autoencoder, it feels goodT
Deep Learning: Running CNN on iOS1 Introduction
As an iOS developer, when studying deep learning, I always thought that I would run deep learning on the iPhone, whether on a mobile phone or using trained data for testing.Because t
Deep Learning (ii) sparse filtering sparse Filtering
Zouxy09@qq.com
Http://blog.csdn.net/zouxy09
I have read some papers at ordinary times, but I always feel that I will slowly forget it after reading it. I did not seem to have read it again one day. So I want to sum up some useful knowledge points in my thesis. On the one hand, my understanding will be deeper, and on the other hand, it will facilitate fut
be used to prevent overfitting when training data is lowDisadvantage: The training time will be extended, but does not affect the test timesome MATLAB functionsUse RNG in 1.matlab to replace the popular interpretation of rand (' seed ', SD), Randn (' seed ', SD) and rand (' state ', SD)ExperimentWhat I did was experiment was repeated deep
learned from pixels through interaction with deformation and occlusion handling models. Such interaction helps to learn more discriminative features.
CitationIf you use our codes or datasets, please cite the following papers:
W. Ouyang and X. Wang. Joint deep learning for pedestrian Detection.In ICCV, 2013. PDF
Code (Matlab code on Wnidows OS)
Caffe of Deep Learning (i) using C + + interface to extract features and classify them with SVM
Reprint please dms contact Bo Master, do not reprint without consent.
Recently because of the teacher's request to touch a little depth of learning and caffe things, one task is to use the ResNet network to extract the characteristics of the dataset and then use SVM t
The deep learning framework Caffe is compiled and installed in Ubuntu.
The deep learning framework Caffe features expressive, fast, and modular. The following describes how to compile and install Caffe on Ubuntu.1. Prerequisites:
CUDA is used for computing in GPU mode.
We recommend that you use the latest versi
Configuring Solver Parameters
Training: such as Caffe Train-solver Solver.prototxt-gpu 0
Training in Python:Document examples:https://github.com/bvlc/caffe/pull/1733Core code:
$CAFFE/python/caffe/_caffe.cppDefine BLOB, Layer, Net, Solver class
$CAFFE/python/caffe/pycaffe.pyNET classes for enhanced functionality
Debug:
Set debug in Make.config: = 1
Set the debug_info:true in Solver.prototxt
Python/matlab
probability estimate. Merging the two best model in Figure 3 and Figure 4 to achieve a better value, the fusion of seven model will become worse.Ten. Reference[1]. Simonyan K, Zisserman A. Very deep convolutional Networks for large-scale Image recognition[j]. ARXIV Preprint arxiv:1409.1556, 2014.[2]. Krizhevsky, A., Sutskever, I., and Hinton, G. E. ImageNet classification with deep convolutional neural net
Preface: Recently, I intend to learn some theoretical knowledge of deep learing in a slightly systematic way, and intend to use Andrew Ng's Web tutorial Ufldl Tutorial, which is said to be easy to read and not too long. But before this, or review the basic knowledge of machine learning, see Web page: http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning. The content is actually ver
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This article refers to the blog:
http://blog.csdn.net/orangehdc/article/details/37763933;http://my.oschina.net/Ldpe2G/blog/275922;http:// blog.csdn.net/sheng_ai/article/details/39971599
]
References: [1] Tsung-han Chan, Kui Jia, Shenghua Gao, Jiwen Lu, Zinan Zeng, and Yi Ma, pcanet:a simple Deep Learning-Baseline F or Image classification? 2014
Thesis Link: http://arxiv.org/abs/1404.3606
Deep Learning paper note (6) Multi-Stage Multi-Level Architecture Analysis
Zouxy09@qq.com
Http://blog.csdn.net/zouxy09
I have read some papers at ordinary times, but I always feel that I will slowly forget it after reading it. I did not seem to have read it again one day. So I want to sum up some useful knowledge points in my thesis. On the one hand, my understanding will be deeper, and on the other hand,
Deep Learning lab1
I plan to paste the six related experiments of Professor Andrew Ng # deep learning # one by one ~
It is estimated that the length of time will be relatively long (after all, Jos's 7-level float has not been completed yet .)
Certificate --------------------------------------------------------------
Introduction to mxnet Deep Learning LibraryAbstract: Mxnet is a deep learning library that supports languages such as C + +, Python, R, Scala, Julia, Matlab, and JavaScript; Support command and symbol programming; Can run on CPU,GPU, clusters, servers, desktops or mobile dev
The goal of this blog is to introduce the introduction of torch
Bloggers use the Itorch interface to write, the following images to show the code.If you can't remember the name of the method can be in the Itorch Point "tab" key will have intelligent input, similar to MATLAB
Simple Introduction to String,numbers,tables
The action of the string is a single quotation mark, and then the print () function in the second row is a bit like the
First the PO on the main Python code (2.7), this code can be found on the deep learning. 1 # Allocate symbolic variables for the data 2 index = T.lscalar () # Index to a [mini]batch 3 x = T.matrix (' x ') # The data is presented as rasterized images 4 y = t.ivector (' y ') # The labels is presented as 1D vector of 5 # [INT] Labels 6 7 # Construct the logistic regression Class 8 #
Deep Learning: 4 (Logistic Regression exercise)-tornadomeet-blog
Deep Learning: 4 (Logistic regression exercises)
Preface:
This section to practice the logistic regression related content, reference for web pages: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php? Course = deeplearning Doc = exer
mainstream framework, of course, not to say that Keras and CNTK are not mainstream, the article does not have any interest related things, but the keras itself has a variety of frameworks as the back end, So there is no point in contrast to its back-end frame, Keras is undoubtedly the slowest. and CNTK because the author of Windows is not feeling so also not within the range of evaluation (CNTK is also a good framework, of course, also cross-platform, interested parties can go to trample on the
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