matlab deep learning toolbox

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Deep Learning Model: CNN convolution neural Network (i) depth analysis CNN

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

Deep Learning Learning Notes (ii): Neural network Python Implementation __python

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:

"Reprint" "code-oriented" Learning deep learning (iv) stacked Auto-encoders (SAE)

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 iOS

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

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

Deep Learning 23:dropout Understanding _ Reading Paper "Improving neural networks by preventing co-adaptation of feature detectors"

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

Joint deep Learning for pedestrian detection notes

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

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 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

Caffe--deep Learning in practice

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

Deep learning Notes (ii) Very Deepin convolutional Networks for large-scale Image recognition

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

Deep Learning: One (basic knowledge _1)

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

"Pcanet:a Simple Deep Learning Baseline for Image Classification" intensive reading notes

[ 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

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 #

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 Library

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

Torch Deep Learning Tutorial (i)

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

[Deep Learning] Python/theano Code Analysis of implementing logistic regression Network

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 exercises)

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

Paddlepaddle, TensorFlow, Mxnet, Caffe2, Pytorch five deep learning framework 2017-10 Latest evaluation

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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