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Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.NB Sp The onli
Source: http://wanghaitao8118.blog.163.com/blog/static/13986977220153811210319/Google's deep-mind team published a bull X-ray article in Nips in 2013, which blinded many people and unfortunately I was in it. Some time ago collected a lot of information about this, has been lying in the collection, is currently doing some related work (want to have a small partner to communicate).First, related articlesOn the DRL, this aspect of the work should be with
Paddlepaddle is Baidu Open source of a deep learning framework, according to its official website of the document used to learn.This article describes its installation.-Operating systemThe official website document uses the operating system is ubunt14.04, I use is the VMware Workstation player installs the Ubuntu virtual machine, it and redhat some different, but the configuration is troublesome, the DNS co
Source: http://tech.163.com/16/0427/07/BLL3TM9M00094P0U.htmlEditor's note: 2016 is the 60 anniversary of Ai's birthday. April 22, the 2016 Global AI Technology Conference (GAITC) and AI 60 commemoration ceremony was held in Beijing National Convention Center, about 1600 experts, academics and industry members attended the conference.The special report of the General Assembly is chaired by the Deputy Secretary-General of China AI Society and Dr. Kaiyu, founder and CEO of Horizon Robotics. Guests
Preface: Today just heard a talk about Extreme learning Machine (Super limited learning machine), the speaker is Elm Huangguang Professor . The effect of elm is naturally much better than the SVM,BP algorithm. and relatively than the current most fire deep learning, it has a great advantage: the operation speed is ve
, when the visibility of the sign is lower, or if a tree blocks part of the logo, its ability to recognize it will fall. Until recently, computer vision and image-detection technology were far from human capabilities because it was too easy to make mistakes. Deep Learning: The technology of realizing machine learning "Artificial Neural Network (Artificial neural
Deep learning part of the direction of Paper, for personal use.a RNN1 Recurrent neural network based language modelThe RNN used in the language model2 statistical Language Models Based on neural NetworksMikolov's doctoral dissertation, which focuses his work on the language model of RNN in tandem3 Extensions of recurrent neural Network Language ModelContinuation of the RNN, some improvements in the network,
Written in Front: it is said that next week will be xxxxxxxx, frighten the baby hurriedly find some advertising things to seeGbdt+lr's model was known before, and Dnn+lr's model was known, but none of them had been tested.The application of deep learning in the ranking of recommended platform for American group reviewsoriginal 2017-07-28 Pan Hui Group Reviews technical Team United States as the largest dom
Reproduced http://blog.csdn.net/zhoutongchi/article/details/8191991 Learning ing functions and literature applied in behavior recognition/image classification (models and non-models are associated with each other, and algorithms are mutually adopted. There is no clear distinction between them, including the bionic literature) %The research focuses on ICA model and deep
combinations, 9 combinations were realized. This method. --1986 Inverse propagation algorithm--1994 long and short memory network--2006 Deep Neural Network--2007 convolutional Neural network 3. Why do you learn so much in depth now?--"Big" dataAt present, the technology development is better, the network has rich data.Deep learning: It takes a lot of data to train his abilities.--"
Deep learning of wheat-machine learning Algorithm Advanced StepEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For
This article for the original article reproduced must indicate the source of this article and attached this article address hyperlink and blog address: http://blog.csdn.net/qq_20259459 and author mailbox (jinweizhi93@gmai.com). (If you like this article, you are welcome to pay attention to my blog or to do a bit of praise, there is a need to mail contact me) As for this article, I really wanted to write about it last week, but I have always felt that it has to be considered before writing. Firs
1. Research background and rationale 1958, Rosenblatt proposed Perceptron model (ANN)In 1986, Hinton proposed a deep neural network with multiple hidden layers (MNN)In the 2006, Hinton Advanced Confidence Network (DBN), which became the main frame of deep learning.Then, the efficiency of this algorithm is validated by Bengio Experiment 2.3 classes of depth learning
, 2, 3,1/2,1/3}, for aspect ratio = 1, add an additional default box, the size of the box. For each default box, the width, height, and center points are calculated as follows:4. Hard negative miningAfter matching, many default boxes are negative samples, which will result in a positive sample, negative sample imbalance, training difficult to converge. Therefore, the paper sorts the negative samples according to the confidence level, selects the highest ones, and guarantees that the proportion o
industry for image classification with KNN,SVM,BP neural networks. Gain deep learning experience. Explore Google's machine learning framework TensorFlow. Below is the detailed implementation details. System Design In this project, 5 algorithms for experiments are KNN, SVM, BP Neural Network, CNN and Migration Learning
probability, the probability that the return type is Softmax, and which highest result is evaluated. If you do a global system assessment, you can then add a layer of accuracy layer, the return type is accuracy. 3.2 2014 googlenet 2014 Imagenet Classification Detection Champion, 22-tier network ... To kneel, interested students to see the structure of the paper, where I can not cut off the screenshot ... In addition, give a few references: 1. Beginners to play: You can use the online convne
Happy New Year! This is a collection of key points of AI and deep learning in 2017, and ai in 2017RuO puxia Yi compiled from WILDMLProduced by QbitAI | public account QbitAI 2017 has officially left us. In the past year, there have been many records worth sorting out. The author of the blog WILDML, Denny Britz, who once worked on Google Brain for a year, combed and summarized the AI and
Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! Important! Important!1. Shallow-layer neural networks and deep learning2. The significance of deep learning, reduce the burden of each layer of network, simplifying complex features. Very effective for complex raw feature learning tasks, such as
Cold Yang small dragon Heart DustDate: March 2016.Source: http://blog.csdn.net/han_xiaoyang/article/details/50856583http://blog.csdn.net/longxinchen_ml/article/details/50903658Disclaimer: Copyright, reprint please contact the author and indicate the source1.Key ContentIntroductionThe system is based on the CVPR2015 of the paper "deep learning of Binary Hash Codes for Fast image retrieval" Implementation of
first, deep reinforcement learning of the bubbleIn 2015, DeepMind's Volodymyr Mnih and other researchers published papers in the journal Nature Human-level control through deep reinforcement learning[1], This paper presents a model deep q-network (DQN), which combines depth
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