deep learning atari

Deep Learning Source code Collection-Continuous update ... Zouxy09@qq.com Http://blog.csdn.net/zouxy09 Collected some source code for deep learning. The main is MATLAB and C + +, of course, there are python. Put it here and follow up with new updates that will continue. The table below is also welcome to be available

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

This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning. Catalogue Perceptual device S-type neurons The architecture of the neural network Using neural networks to recognize handwritten numbers Towards Deep learning Perceptron (perceptrons)1. Fundament

ObjectiveFor deep learning, novice I recommend to see UFLDL first, do not do assignment words, one or two nights can be read. After all, convolution, pooling what is not a particularly mysterious thing. The course is concise, sharply, and points out the most basic and important points.cs231n This is a complete course, the content is a bit more, although the course is computer vision, but 80% is the content

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

Recently studied a few days of deep learning of the MATLAB Toolbox code, found that the author gives the source of the comments is very poor, in order to facilitate everyone to read, the code has been commented, share with you.Before reading the MATLAB Toolbox code, we recommend that you read a few CNN two classic materials, the convolutional neural Network MATLAB Toolbox Code understanding is very helpful,

the first week after-school assignment is a 10-course choice question Note: The answer is from the first one and then the ABCD ... The answer has its own understanding, there are also from the online blog reference, only to learn.1. First questionI understand the answer: D.Reference answer: A. "AI is the new power", this is the topic of Wunda Teacher's speech on AI conference this year. Of course, the analogy is that AI, like electricity 100 years ago, is bringing great changes to our productiv

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 keep pace. In the area of data mining, Pytho

/* author:cyh_24 *//* date:2014.10.2 *//* Email: [Email protected] *//* more:http://blog.csdn.net/cyh_24 */Recently, the focus of the study in the image of this piece of content, the recent game more, in order not to drag the hind legs too much, decided to study deeplearning, mainly in Theano the official course deep Learning tutorial for reference.This series of blog should be continuously updated, I hope

When does the deep learning model in NLP need a tree structure?Some time ago read Jiwei Li et al and others [1] in EMNLP2015 published the paper "When is the Tree structures necessary for the deep learning of representations?", This paper mainly compares the recursive neural network based on tree structure (Recursive n

Write in front:has not tidied up the habit, causes many things to be forgotten, misses. Take this opportunity to develop a habit.Make a collation of the existing things, record, to explore and share new things.So the main content of the blog for I have done, the study of the collation of records and new algorithms, network framework of learning. It's basically about deep

closer to the real neuron activation model. Bridging the gap with pre-training 2 about pre-training in deep learning 2.1 Why pre-training Deep networking has the following drawbacks: The deeper the network, the more training samples are needed. If the use of supervision will require a large number of samples, or small-scale samples can easily lead to overfitting

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

, momentum=0.9, decay=0.0, Nesterov=false) model.fit (train_set_x, train_set_y, validation_split=0.1, nb_epoch=200, batch_size=256, Callbacks=[lrate]) The above code is to make the learning Rate index drop, as shown in the following figure: Of course, can also directly modify the parameters in the SGD declaration function to directly modify the learning rate, learning