deep learning framework

1. Why add pooling (pooling) to the convolutional networkIf you only use convolutional operations to reduce the size of the feature map, you will lose a lot of information. So think of a way to reduce the volume of stride, leaving most of the information, through pooling to reduce the size of feature map.Advantages of pooling:1. Pooled operation does not increase parameters2. Experimental results show that the model with pooling is more accurateDisadvantages of pooling:1. Because the stride of t

CSS deep understanding of learning notes-margin and css learning notes-margin 1. margin and container size Element size: ① visible size clientWidth (standard); ② occupying size 　　 Margin and visual size: ① applicable to normal block elements without width/height; ② applicable only to horizontal dimension Margin and occupy size: ① block/inline-block horizontal ele

Deep understanding of CSS learning notes border and css learning notes 1. border-width Border-width does not support percentages: semantics and scenarios are determined. In reality, the concepts of borders do not support percentages. Border-width supports keywords: thin, medium (default), and thick. The values are 1px, 3px, and 5px (except IE7 ). Why is the defau

above. Move right to erase the non-0-bit to the right of the decimal points of the result. These non-0 bits are actually positive, but because they are erased, the result subtracts the values of the non-0 bits represented by the original negative result, and the final result is rounded down rather than rounded to 0. Floating point number: Standard for representing floating-point numbers and their operations: IEEE Standard 754. Floating-point numbers are normalized, non-nor

dramatically. The most important thing is that there is no way to use the framework of deep learning.3. Use the Python process to run a trained model in deep learning and invoke the services provided by the Python process in a Java application. This method I think is the be

Deep Learning, also known as unsupervised feature learning or feature learning, is a hot topic at present. This article mainly introduces the basic idea and common methods of deep learning. 1. What is

21. Application of Depth neural network in visual significance (visual Attention with deep neural Networks) (English, conference papers, 2015, IEEE Search)This article focuses on the application of CNN in the field of significance detection.　　22. Progress in deep learning Research (Chinese, Journal, 2015, net)A summary article on

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:

engineering bug all a bunch of nolearn+theano+lasagne you ask questions here, I guess a little bit of mxnet is coming. The problem is recursion until the stack explodes! PYLEARN2 has stopped development, did not pay attention to, if mainly in order to use custom good module, Keras extremely convenient, easy to get started, update frequency is also good, now in addition to Theano also support TensorFlow, there are problems can be asked in keras-users or GitHub ; Lasagne no use, blocks can direct

Transferred from: http://baojie.org/blog/2013/01/27/deep-learning-tutorials/A few good deep learning tutorials, with basic videos and speeches. Two articles and a comic book are attached. There are some additions later.Jeff Dean @ StanfordHttp://i.stanford.edu/infoseminar/dean.pdfAn introductory introduction to what DL

training, scale presents a problem for deep learning. The need to fully interconnect neurons, particularly in the upper layers, requires immense compute power. The first layer for an image-processing application could need to analyze a million pixels. The number of connections in the multiple layers of a deep network would be the orders of magnitude greater. "Th

Deep learning with STRUCTURECharlie Tang is a PhD student in the machine learning group at the University of Toronto, working with Geoffrey Hinton andRuslan Salakhutdinov, whose the interests include machine learning, computer vision and cognitive science. More specifically, he had developed various higher-order extens

The article was transferred from the deep learning public numberDeep learning is a new field in machine learning that is motivated by the establishment and simulation of a neural network for analytical learning of the human brain, which mimics the mechanisms of the human bra

Deep convolutional neural networks have been a great success in the field of image, speech, and NLP, and from the perspective of learning and sharing, this article has compiled the latest resources on CNN related since 2013, including important papers, books, video tutorials, Tutorial, theories, model libraries, and development libraries. At the end of the text is attached to the resource address. Importan

Mobileye and Nvidia use a convnet based approach in their upcoming automotive Vision systems. Other increasingly important applications relate to natural language understanding and speech recognition. Despite these achievements, Convnets was largely abandoned by the mainstream computer vision and machine learning community until the Imagenet race in 2012. When the deep convolution network was applied to da

The history of CNNIn a review of the 2006 Hinton their science Paper, it was mentioned that the 2006, although the concept of deep learning was proposed, but the academic community is still not satisfied. At that time, there was a story of Hinton students on the stage when the paper, machine learning under the Taiwan Daniel Disdain, questioned your things have a

First spit groove, deep learning development speed is really fast, deep learning framework is gradually iterative, it is really hard for me to engage in deep learning programmer. I bega

Directory 1. srcnn Contribution Inspiration Network O. pre-processing I. Patch extraction and representation II. Non-linear Mapping III. Reconstruction Story Further learning 1. srcnn Home pageHttp://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html ECCV , Tpami . Contribution End-to-end Deep