The 2016 is a very important historical node, signifying that the AI system of unity of knowledge and line will go to the historical stage. It changes not only the next go, it will change a lot of things. --KaiyuOn the "Adas and autonomous Driving Trends forum" of the "2016 Smart cars and Shanghai Forum", Dr. Kaiyu, founder and CEO of Horizon Robotics, delivered a keynote speech entitled "The road to autonomous driving based on deep
The theme report of "Transfer model of deep learning" shorthand and commentary (iv) Bai Chu of the Red bean Family concern 2017.11.04 22:33* 3275 reading 141 comments 0 like 0
The author presses: machine learning is moving towards a new era of interpretive models based on "semantics". Migration learning is likely to ta
Original URL: http://www.iteye.com/news/312701. We should see deeper models, which can be learned from fewer training samples compared to today's models, and will make substantial progress in unsupervised learning. We should see more accurate and useful speech and visual recognition systems.2. I expect deep learning to be increasingly used for multi-mode (multi-m
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 goodThe contractive autoencoders model is:whichThe
Structured Query language to manipulate database.for example:1. INSERT into Events VALUES ("rubyconf", 100); Insert a piece of data into the events table2. SELECT * from events; Take out all the dataTri ACID (4 properties)Transaction: A process of doing business. Package a set of actions to execute together.Use begin;...commit; it can guarantee the correctness of data access, either succeed together or fail together.Atomicity: A transaction is an atom.Consistency: Consistency ensures that the i
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
())--Data into H2O format (AS.H2O)-Model Fit (h2o.deeplearning)-Prediction (H2O.PREDICT)-Data rendering (h2o.performance).One, H2O package demo (GLM)Online already have, blog author read and do a simple Chinese comment. Details can be found in std1984 blog.second, the case from PARALLELR blogThe main purpose of the blog is to show that deep learning is more accurate than other common
process the image and convert it to the vector format of x [400.
As long as the pixels of the image can be read and converted. We can consider using opencv for implementation.
Here, my method is to convert the image to a 20*20 pixel image after drawing a number by hand, as shown in the lower right corner, and then convert the image in the lower right corner to an array of 400, enter the result of predict.3 Method 2: Use DeepBeliefSDK
Https://github.com/jetpacapp/DeepBeliefSDKThis is a
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
+ */ surveytoken.unbind (); the return(surveyid%2) ==0? " Even ":" Odd ";//Returns the even string if it is an even number, or odd string if it is an odd number * } $SYSTEM.OUT.PRINTLN ("Survey object is empty");Panax Notoginseng return NULL; - } the +}The policy of the routed data source is determined in the overridden method: If the survey ID is even, it is saved to the answer table in the main library
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
The recent deep learning fire not only attracted the attention of the academic community, but also sought after in the industry. In many important evaluations, DL has achieved the effect of state of the art. Especially in terms of speech recognition, DL has reduced the error rate by about 30% and has made significant progress. If the company that uses speech recognition does not use DL, I am sorry to say he
A summaryIn this paper, we present a very simple image classification deep learning framework, which relies on several basic data processing methods: 1) Cascade principal component Analysis (PCA), 2) Two value hash coding, 3) chunking histogram. In the proposed framework, the multi-layer filter kernel is first studied by PCA method, and then sampled and encoded using two-valued hash coding and block histogr
density-kernel density estimators for Juliadimensionality Reduction-methods for dimensionality reductionNmf-a Julia package for non-negative matrix factorizationAnn-julia Artificial Neural NetworksMocha-deep Learning framework for Julia inspired by CaffeXgboost-extreme gradient boosting Package in JuliaManifoldlearning-a Julia Package for manifold learning and n
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
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
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