deep learning goodfellow

Preface At present, deep learning to grab enough eyeballs and attention, from the layout of major companies, to the springing out of a wave of start-up companies, and then to all kinds of popularization, in-depth analysis of the relevant public number, every day there are a large number of technology, paper interpretation related articles, blogs, etc., a variety of information such as flooding into our vis

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

Neural network and deep learning the book has been read several times, but each time there will be a different harvest.The paper of DL field is changing rapidly. There's a lot of new idea coming out every day, I think. In-depth reading of classic books and paper, you will be able to find Remian open problems. So there's a different perspective.Ps:blog is a summary of important contents in the main extract b

), matrix decomposition (matrices factorization). In applying the compression perception process, we find that most of the signals themselves are not sparse (that is, the expression in the natural base is not sparse). But after a proper linear transformation is sparse (that is, the other group of bases (basis) or frames (frame, I do not know how to translate) are sparse). such as harmonic extraction (harmonic retrieval), the time domain signal is not sparse, but in the Fourier domain signal is

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

1. Current situation:Deep learning is now very hot, and all kinds of meetings have to be stained with this point. Baidu Brain, Google's brain plan to engage in this. In some areas have achieved very good results, chip recognition, speech recognition, in the security field and even the identification of encryption protocols. The accuracy of the lab in the field of speech is over 90%.2. The essence of deep le

Original link: https://www.paypal-engineering.com/tag/data-science/absrtact: with the explosive growth of data and thousands of machine clusters, we need to adapt the algorithm to run in such a distributed environment. Running machine learning algorithms in a common distributed computing environment has a number of challenges. This article explores how to implement and deploy deep

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

Recently, Google published in the Journal of the American Medical Council titled "Development and Validation of a deep learning algorithm for Detection of diabetic retinopathy in Reti NAL Fundus Photographs "is a deep learning algorithm that Google researchers have put forward to explain the signs of diabetic retinopat

First, it's up to the father of Ai, Turing. Turing once had a dream uninstall "computer and Intelligence" (1950) article, if one day, the computer can do, across the wall, you do not know the opposite and you communicate is a person or computer, then this computer has artificial intelligence. For the next half century, Ai has not developed much. Although the computer has the powerful memory and the data processing ability, but does not have the human cognition ability. For example, Wang, Meo

feature algorithms, our goal is usually to isolate the variables that explain the observed data.Deep learning allows a computer to construct complex concepts through simpler concepts. (The examples in the comparison book can be understood clearly)The idea of learning the correct representation of data is a point of view for explaining deep

Source: Michael Nielsen's "Neural Network and Deep leraning"This section translator: Hit Scir master Xu Zixiang (Https://github.com/endyul)Disclaimer: We will not periodically serialize the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be reproduced."This article is reproduced from" hit SCIR "public number, reprint has obtained consent. " Using neural networks

matching is no longer effective, and then the OCR algorithm is difficult to parse the results.In recent years, The Deep Neural Network (DNN) has been proved to be a powerful recognition capability in the field of image recognition. The identification of single text is a typical classification problem. The usual practice is to train a deep neural network, the last layer of the network is divided into n cate

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

time series signals. CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. CNNs as a deep learning architecture is proposed to minimize the preprocessin