coursera neural networks

"Aggregated residual transformations for Deep neural Networks" is saining Xie and other people in 2016 in the public on the arxiv:Https://arxiv.org/pdf/1611.05431.pdf Innovation Point1. The use of group convolution on the basis of traditional resnet, without increasing the number of parameters under the premise of obtaining a stronger representation ability NamedThis paper presents a resnet improved network

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 network models, FAS

than the exercises, and you'll likely struggle to solve some problems. That's annoying, but, of course, patience in the face of such frustration are the only-to truly understand and internal Ize a subject.With this said, I don ' t recommend working through all the problems. What ' s even better are to find your own project. Maybe want to use neural nets to classify your music collection. Or to predict stock prices. Or whatever. But find a project, ab

Writing back-propagation neural networks using java (III)Confucius said, I am in the three provinces of Japan. If we deal with programs, in addition to three provinces a day, we need to save my code three days a day. Check whether the code can be simpler, easier to understand, easier to expand, more common, whether the algorithm can be optimized, and whether the structure can be abstracted. The code is more

=Datetime.datetime.now ()Print("Time Cost :") Print(Tend-tstart)Analysis:1. Forward Propagation: for in range (1, Len (synapselist), 1): Synapselist is a weight matrix.2. Reverse propagationA. Calculating the error of the output of the hidden layer on the inputdef GETW (Synapse, Delta): = [] # traverse the hidden layer each hidden unit to each output weight, such as 8 hidden units, each hidden unit two output each has 2 weights for in Range (Synapse.shape

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

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.

], nb_conv[2], nb_conv[2]), padding='same', activation='Relu', Kernel_regularizer=regularizers.l2 (0.01) ) (C05) c07= Batchnormalization (epsilon=1e-06, momentum=0.9, weights=None) (C06) C08= Spatialdropout3d (0.5) (c07) c09=Flatten () (C08) c010= Dense (+, kernel_initializer='Glorot_normal', activation='Relu', Kernel_regularizer=regularizers.l2 (0.01) ) (c09) c011= Dense (nb_classes, kernel_initializer='Glorot_normal', Kernel_regularizer=regularizers.l2 (0.01) ) (c010) c012= Activation ('Softma

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.-Maintain the original input and output of th

The process of convolution is the process of extracting the corresponding feature, and obtains the high dimensional eigenvector.The process of deconvolution is in fact a sparse coding process, which is to restore the feature vectors obtained by convolution to the original input image by weighting About dilate convolution visible this blog post https://zhuanlan.zhihu.com/p/23795111 and https://github.com/vdumoulin/conv_arithmeticI think since dilate convolution can change the size of the kernel

A recent article on data enhancement is more interesting: here is the core code implementation and implementation details, which can be accessed by itself:Training neural Networks with Very Little data–aThe general meaning of the article is to transform the Cartesian coordinate system into the image in polar coordinate system through some transformation, which is directly given by the following formula: The