theano deep learning

Deep learning est mort. Vive differentiable programming! This English-French mixed words, translated into Chinese, is "deep learning is dead, can be differential programming long live." It is one of the big three in deep learning:

/ * copyright notice: Can be reproduced arbitrarily, please be sure to indicate the original source of the article and author information . */Author: Zhang JunlinTimestamp:2014-10-3This paper summarizes the application methods and techniques of deep learning in natural language processing in the last two years, and the related PPT content, please refer to this link, and the main outline is listed here

CNN began in the 90 's lenet, the early 21st century silent 10 years, until 12 Alexnet began again the second spring, from the ZF net to Vgg,googlenet to ResNet and the recent densenet, the network is more and more deep, architecture more and more complex, The method of vanishing gradient disappears in reverse propagation is also becoming more and more ingenious.　　　　 LeNet AlexNet Zf Vgg Googlenet ResNet Densenet

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 paper notes (vii) Visualization of high-level features in depth networks Zouxy09@qq.com Http://blog.csdn.net/zouxy09 I usually read some papers, but the old feeling after reading will slowly fade, a day to pick up when it seems to have not seen the same. So want to get used to some of the feeling useful papers in the knowledge points summarized, on the one hand in the process of finishing, t

Course Description: This is an introductory course on deep learning, and deep learning is mainly used for machine translation, image recognition, games, image generation and more. The course also has two very interesting practical projects: (1) Generate music based on RNN (2) Basic X-ray detection, GitHub address: Http

, we can directly use the full connection of the neural network, to carry out the follow-up of these 120 neurons, the following specific how to do, as long as the knowledge of multi-layer sensors understand, do not explain. The above structure, is only a reference, in the real use, each layer feature map needs how many, volume kernel size selection, as well as the pool when the sample rate to how much, and so these are changes, this is called the CNN tuning, we need to learn flexible. For exampl

Second lecture: Simple word vector representation: Word2vec, Glove (easy word vector representations:word2vec, Glove)Reprint please specify the source and retention link "I love Natural Language processing": http://www.52nlp.cnThis article link address: Stanford University deep Learning and Natural language processing second: Word vectorRecommended Reading materials: paper1:[distributed representat

Document directory 1.1 how to restrict the use of the Polman machine (RBM) 1.2 restricted Polman machine (RBM) Energy Model 1.3 from energy model to probability 1.4 Maximum Likelihood 1.5 Sampling Method Used 1.6 introduction to Markov Monte Carlo References RBM for deep learning Reading Notes Statement: 1) I saw a statement from other blogs such as @ zouxy09, and the old man copied it. 2) This blo

usually used only when there are a large number of annotated training data. In such cases, fine tuning can significantly improve the performance of the classifier. However, if there are a large number of unlabeled datasets (for unsupervised feature learning/pre-training), there are only relatively few annotated training sets, and the effect of fine tuning is very limited.The previously mentioned network is generally three layers, the following is a g

The 1th chapter introduces the course of deep learning, mainly introduces the application category of deep learning, the demand of talents and the main algorithms. This paper introduces the course chapters, the course arrangement, the applicable crowd, the prerequisites and the degree to be achieved after the completio

Reprinted from Alchemy Laboratory: https://zhuanlan.zhihu.com/p/24720954 I have previously written an article about deep learning training skills, which includes some of the assistant experience: Deep learning training experience. However, as a result of the general deep

Unsupervised learning Using generative adversarial Training and Clustering–authors:vittal Premachandran, Alan L. Yuille An information-theoretic Framework for Fast and robust unsupervised learning via neural Population Infomax–authors:wenta o Huang, Kechen Zhang Unsupervised Cross-domain Image generation–authors:yaniv Taigman, Adam Polyak, Lior Wolf Unsupervised perceptual Rewards for imitation

completed at the L-1 level and can be used directly here.We then find the bias of the error to the input of the L-2 layer:Style 8.11The last item of formula 8.11 can be obtained by Formula 8.10.Next, the error is biased to the first connection weight value: Here we assume that the neuron number of the l-3 layer is GStyle 8.12The last item of formula 8.12 can be calculated by formula 8.11, so the weight adjustment formula is:Style 8.13At this point, we have completed the BP algorithm derivation

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology, application interested students to join.Recently, while reviewing the classical machine learning algorithms, we also looked at some typical algorithms of deep learning.

You know, unlike machine learning models, deep learning models are filled with a variety of hyper-parameters. Moreover, not all parametric variables have the same contribution to the learning process of the model.Given this extra complexity, it is not easy to find the optimal configuration of these parameter variables

Directory I. Overview II. Degradation Iii. Solution deep Residual learning Iv. Implementation Shortcut connections Home pageHttps://github.com/KaimingHe/deep-residual-networks TensorFlow implementation:Https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet In fact, TensorFlow has built-in ResNet:https://

Reference documents:Feature Extraction:In deep learning, the amount of information that the lower layer carries is greater than the amount of information on top . The lowest layer is considered a base. For example, in high-dimensional space, there is always a set of complete bases. Any vector can be represented by a complete base line. This is, after a multilayer representation, the rank of the matrix of th

The previous article mentions the difference between data mining, machine learning, and deep learning: http://www.cnblogs.com/charlesblc/p/6159355.htmlDeep learning specific content can be seen here:Refer to this article: Https://zhuanlan.zhihu.com/p/20582907?refer=wangchuan "Wang Chuan: How

JS doing deep learning, accidental discovery and introductionRecently I first dabbled with node. js, and used it to develop a graduation design Web module, and then through the call System command in node execution Python file way to achieve deep learning function module docking, Python code intervention, make JS code