convolutional neural network tutorial

visual comprehension of convolutional neural networks The first to suggest a visual understanding of convolutional neural Networks is Matthew D. Zeiler in the visualizing and understanding convolutional Networks. The following two blog posts can help you understand this a

ImageNet classification with deep convolutional neural Networks reading notes(after deciding to read a paper each time, the notes are recorded on the blog.) ）This article, published in NIPS2012, was Hinton and his students, in response to doubts about deep learning, used deep learning for imagenet, the largest database of image recognition, and eventually achieved very surprising results, The result is much

. Pretreatment: Mean removal;whitening (ZCA) Enhanced generalization capability: Data augmentation;weight regularization; adding noise to the network, including dropout,dropconnect,stochastic pooling. Dropout: The output of some neurons in the fully connected layer is randomly set to 0 at the full connection layer only. Dropconnect: Also only used on the full-connection layer, Random binary mask on weights. Stochastic Pooli

1, IntroductionDL solves VO problem: End-to-end vo with RCNN2. Network structureA.CNN based Feature ExtractionThe paper uses the Kitti data set.The CNN section has 9 convolutional layers, with the exception of CONV6, the other convolutional layers are connected to 1 layers of relu, and there are 17 layers.B, RNN based sequential modellingRNN is different from CNN

0-Background The so-called style conversion is based on a content image and a style image, merging the two, creating a new image that combines both contents and style.The required dependencies are as follows: Import OS import sys import scipy.io import scipy.misc import Matplotlib.pyplot as Plt from Matplotlib.pyplot import imshow from PIL import Image from nst_utils import * import NumPy as NP import te Nsorflow as TF %matplotlib inline 1-transfer Learning Migration learning is the applicat

I. Documentation names and authorsconvolutional neural Networks at Constrained time COST,CVPR two. Reading timeJune 30, 2015Three. Purpose of the documentThe author hopes to improve the accuracy of CNN by modifying the model depth and the parameters of the convolution template, while maintaining the computational complexity. Through a lot of experiments, the author finds the importance of different parameters in the

scientists have contributed significantly to the success of convolutional networks?There is no doubt that the neuro-cognitive machine (Neocognitron) proposed by Japanese scholar Kunihiko Fukushima has enlightening significance. Although the early forms of convolutional networks (Convnets) did not contain too many Neocognitron, the versions we used (with pooling layers) were affected.This is a demonstration

affected.This is a demonstration of the mutual connection between the middle layer and the layers of the neuro-cognitive machine. Fukushima K. (1980) in the neuro-cognitive machine article, the self-organizing neural network model of pattern recognition mechanism is not affected by the change of position.Can you recall the "epiphany" moments or breakthroughs that occurred in the early days of

set, the KL distance is the indicator that describes the diversity, thus reducing the amount of computation. Traditional deep learning will need to do before the training of data enhancement, each sample is equal; This article contains some data enhancement not only does not play a good role, but brings the noise, it needs to do some processing, but also some of the data does not need to be enhanced, which reduces noise and saves calculation. Qa Q: Why did the active learning not b

Wang, Min, Baoyuan Liu, and Hassan Foroosh. "Factorized convolutional neural Networks." ArXiv preprint (2016). This paper focuses on the optimization of the convolution layer in the deep network, which has three unique features:-Can be trained directly . You do not need to train the original model first, then use the sparse, compressed bits and so on to compress.

. We use the cublas. lib and curand. Lib libraries. One is matrix calculation and the other is random number generation. I applied for all the memory I needed at one time. After the program started running, there was no data exchange between the CPU and GPU. This proved to be very effective. The program performance is about dozens of times faster than the original C language version (if the network is relatively large, it can reach a speed-up ratio of

of the "object" in the "the position with the maximum score Use a cost function this can explicitly model multiple objects present in the image. Because there may be many objects in the graph, the multi-class classification loss is not applicable. The author sees this task as multiple two classification questions, loss function and classification score as followsTrainingMuti-scale TestExperimentClassification MAP on VOC test: +3.1% compared with [56] MAP on VOC test: +7.

Minimalist notes Deepid-net:object detection with deformable part Based convolutional Neural Networks Paper Address Http://www.ee.cuhk.edu.hk/~xgwang/papers/ouyangZWpami16.pdf This is the CUHK Wang Xiaogang group 2017 years of a tpami, the first hair in the CVPR2015, increased after the experiment to cast the journal, so the contrast experiment are some alexnet,googlenet and other early

= Sess.run ([Me Rged, Accuarcy], feed_dict=feed_dict (False)) Test_writer.add_summary (summary, i) print (' Accuracy a T step%s:%s '% (I, ACC)) else:if i%100 = = 99:continue Run_options = Tf. Runoptions (TRACE_LEVEL=TF. Runoptions.full_trace) Run_metadata = tf. Runmetadata () Summary, _ = Sess.run ([merged, Train_step], feed_dict=feed_dict (True), Options=run_options, Run_metadata=run_metadata) train_writer.add_run_metadata (run_metadat A, ' step%03d '%i) t

ImageNet classification with deep convolutional neural Networks reading notes(2013-07-06 22:16:36) reprint Tags: deep_learning imagenet Hinton Category: machine learning (after deciding to read a paper each time, the notes are recorded on the blog.) )This article, published in NIPS2012, is Hinton and his students are using deep learning in response to doubts about deep learn

alexnet Summary Notes Thesis: "Imagenet classification with Deep convolutional neural" 1 Network Structure The network uses the logic regression objective function to obtain the parameter optimization, this network structure as shown in Figure 1, a total of 8 layer

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 learning Model: the derivation and implementation of CNN convolution neural network[4] Deep learning Model: the reverse derivation and practice of

very interesting. He said, what is convolution? For example, the constant bending of a wire, assuming that the heating function is f (t), and that the heat dissipation function is g (t), the temperature at this moment is the convolution of f (t) and g (t). In a given environment, the sound source function of the sound body is f (t), and the reflection effect function of the sound source is g (t), then the receiving voice is the convolution of f (t) and g (t) in this environment. Without conside

neurons are active, only a very small fraction will be active, the different layers of neurons can not be fully connected. In the back of 5.5.6, we will see an example of the sparse network structure used by convolutional neural networks.We can naturally design a more complex network structure, but in general we have

This article first Huchi: HTTPS://JIZHI.IM/BLOG/POST/INTUITIVE_EXPLANATION_CNN What is convolutional neural network. And why it's important. convolutional Neural Networks (convolutional neu