deep learning recommender system

artificial intelligence platform. such as FACKBOOK,IBM technology companies have vigorously developed the field of artificial intelligence in automatic driving, image recognition, speech recognition and other fields. 2) Artificial Neural network The realization of artificial intelligence, a large part is based on artificial neural network. Artificial neural Network (Artificial Neural Networks, abbreviated as Anns) is also referred to as Neural Network (NNS), which simulates the behavioral chara

to be personal, but it's easy to look at SAS help. The PDV mechanism of SAS and the execution mechanism of macros must be understood. SAS has a great advantage, the standard of unification, as long as the learning to be able to swim throughout the system. R VS python: In contrast, R is statistically much stronger than Python because Statsmodel does not give force, and new statistical methods Python cannot

, Wickramasinghe N, et al deepr:a convolutional Net for Medical Records[j]. ArXiv preprint arxiv:1607.07519, 2016. [3]nie L, Wang M, Zhang l, et al disease inference from health-related questions via sparse deep learning[j]. IEEE transactions on Knowledge and Data Engineering, 2015, 27 (8): 2107-2119. [4]savova G K, Masanz J J, Ogren P V, et al. Mayo Clinical Text Analysis and Knowledge Extraction

SQL is a standard language for storing, manipulating and retrieving data in Databasee.Relational database: RDBMS (relational database mangement System)SQLite3 :The Rails default lightweight database, integrated in rails, is db/development.sqlite3 in this file.Use: Single use. So when the actual deployment will be replaced by MySQL and other database server.Mysql:The current popular open source database. This is a database server, to connect it require

Setting up a deep learning machine from Scratch (software)A detailed guide-to-setting up your machine for deep learning. Includes instructions to the install drivers, tools and various deep learning frameworks. This is tested on a

millions of pictures, until the weight of the artificial neuron input is highly accurate, so that every judgment is correct-whether it's fog or fog, it's sunny or rainy. At this point the neural network can "teach" themselves, to find out what the stop sign is exactly, it can also identify Facebook face images, can identify the cat-Wunda (Andrew Ng) in 2012, Google did the thing is to let the neural network to identify the cat.Wunda's breakthrough is to make the neural network extremely large,

companies want to go to the company to brush the work card, but here we do not need it, using face recognition, see what I can do. When I come close, it will recognize my face and then say "Welcome" (Andrew NG), I can pass without a work-cards. Let's take a look at another situation, next to Lin Yuanqing, IDL (Baidu Deep Learning Laboratory) Director, he led the development of the face recognition

variables, and the parent process has also seen this modification. The vfork function may occur because the fork of the early system did not implement the write-time replication technology, resulting in a lot of useless work in each fork call (in most cases, it is called exec to execute a new program after fork) the efficiency is not high, so the vfork function is created. The current implementation basically uses the write-time replication technolog

In the previous sections, we have covered what is target detection and how to detect targets, as well as the concepts of sliding windows, bounding box, and IOU, non-maxima suppression.Here will summarize the current target detection research results, and several classical target detection algorithms to summarize, this article is based on deep learning target detection, in the following sections, will be spe

probability estimate. Merging the two best model in Figure 3 and Figure 4 to achieve a better value, the fusion of seven model will become worse.Ten. Reference[1]. Simonyan K, Zisserman A. Very deep convolutional Networks for large-scale Image recognition[j]. ARXIV Preprint arxiv:1409.1556, 2014.[2]. Krizhevsky, A., Sutskever, I., and Hinton, G. E. ImageNet classification with deep convolutional neural net

7 mins version:dqn for Flappy Bird Overview This project follows the description of the "Deep Q Learning algorithm described" Playing Atari with deep reinforcement L Earning [2] and shows that this learning algorithm can is further generalized to the notorious Flappy Bird. installation Dependencies: Python 2.7 or 3 Ten

: deep learning has made great progress in vision and speech, attributed to the ability to automatically extract high level features. The current reinforcement learning successfully combines the results of deep learning, that is, DQN, to get breakthrough on Atari games.Howev

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

Preface: Recently, I intend to learn some theoretical knowledge of deep learing in a slightly systematic way, and intend to use Andrew Ng's Web tutorial Ufldl Tutorial, which is said to be easy to read and not too long. But before this, or review the basic knowledge of machine learning, see Web page: http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning. The content is actually ver